Using interactional and linguistic analysis to distinguish between epileptic and psychogenic nonepileptic seizures: a prospective, blinded multirater study

Face-to-face and telephone appointments in the seizure clinic: A comparative analysis based on recorded interactions

OBJECTIVE

Telephone appointments are now widely used in seizure treatment, but there is little understanding of how they compare to face-to-face appointments. Studies from other fields suggest that comparisons can be done on three levels: 1) Abstract level: duration of appointment. 2) Structural level: distribution of talk. 3) Detailed level: aspects of communication. This study aims to compare seizure clinic face-to-face and telephone appointments based on their duration, distribution of talk, and the number of questions asked by patients/companions.

METHODS

Statistical comparison between recordings and transcripts of 34 telephone appointments (recorded in 2021) and 56 face-to-face appointments (recorded in 2013).

RESULTS

There was no significant difference between the duration of face-to-face (median: 16.5 min) and telephone appointments (median: 16.2 min). There was no significant difference in the ratio of neurologist to patient/companion talk (face-to-face: 55% vs. 45%, telephone: 54% vs. 46%). Patients/companions asked significantly more questions per minute in face-to-face (median: 0.17) than telephone appointments (median: 0.06, p < 0.05).

CONCLUSION

At a broad level, seizure clinic face-to-face and telephone appointments are similar. Examining the details of the interaction, however, reveals important differences in questioning.

PRACTICE IMPLICATIONS

Practitioners could take steps to facilitate patient questioning in telephone appointments.

Maladaptive personality traits in patients with epilepsy and psychogenic non-epileptic seizures

OBJECTIVE

We aimed to evaluate the sociodemographic, clinical, and psychological characteristics associated with psychogenic non-epileptic seizures (PNES) in patients with epilepsy, with particular emphasis on the personality profile assessed from a dimensional perspective.

METHODS

The cohort study included 77 consecutive inpatients with active epilepsy aged 36-55 years; 52 (67.5%) were female. The presence of PNES was confirmed by video-EEG monitoring. All patients underwent the Mini-International Neuropsychiatric Interview to diagnose psychiatric disorders. All participants completed the Neurological Disorders Depression Inventory in Epilepsy, the Epilepsy Anxiety Survey Instrument - brief version, and the Personality Inventory for DSM-5 and ICD-11 Brief Form Plus Modified. Chi-square and Fisher's exact tests were used to compare categorical variables, and the Brunner-Munzel test was used for quantitative variables.

RESULTS

Twenty-four patients (31.2%) had both epilepsy and PNES. There were no significant differences in social, demographic or clinical characteristics, psychiatric diagnoses or depression severity. Compared to patients with epilepsy alone, patients with epilepsy and PNES had higher anxiety scores and more pronounced maladaptive personality traits such as disinhibition and psychoticism.

SIGNIFICANCE

The main novelty of our study is that using the recently proposed dimensional approach to personality disorders and an appropriate instrument we assessed all personality domains listed in two of the most widely used classifications of mental disorders (DSM-5 and ICD-11) in PWE with and without PNES. To our knowledge, this is the first study to demonstrate the association of the maladaptive traits of psychoticism and disinhibition with the development of PNES in PWE.

Predicting the cause of seizures using features extracted from interactions with a virtual agent

OBJECTIVE

A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions.

METHOD

Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: (1) formulation effort, (2) the proportion of words from different semantic categories, and (3) verb, adverb, and adjective usage.

RESULTS

76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8 % of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5 % by improving the differential diagnosis between epilepsy and FDS.

CONCLUSION

These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

Metaphoric language in the differential diagnosis of epilepsy and psychogenic non-epileptic seizures: Time to move forward

Conversation analysis (CA) to identify metaphoric language (ML) has been proposed as a tool for the differential diagnosis of epileptic (ES) and psychogenic nonepileptic seizures (PNES). However, the clinical relevance of metaphoric conceptualizations is not clearly defined. The current study aims to investigate the ML utilized by individuals with ES and PNES in a pulled multi-country sample. Two blinded researchers examined the transcripts and videos of 54 interviews of individuals (n = 29, Italy; n = 11, USA; n = 14, Russia) with ES and PNES, identifying the patient-seizure relationship representative of the patient's internal experience. The diagnoses were based on video-EEG. Metaphors were classified as "Space/place", "External force", "Voluntary action", and "Other". A total of 175 metaphors were identified. No differences between individuals with ES and PNES were found in metaphoric occurrence (χ2 (1, N = 54) = 0.07; p = 0.74). No differences were identified when comparing the types of metaphors utilized by participants with ES and those with PNES. Patients with PNES and ES did not demonstrate differences in terms of occurrence and categories in ML. Therefore, researchers and clinicians should carefully consider the use of metaphor conceptualizations for diagnostic purposes.

[Functional neurological disorders: A clinical anthology]

Functional neurological disorders have a broad phenotypic spectrum and include different clinical syndromes, which are sometimes associated to each other or appear consecutively over the course of the disease. This clinical anthology provides details on the specific and sensitive positive signs that are to be sought in the context of a suspected functional neurological disorder. Beside these positive elements leading to the diagnosis of functional neurological disorder, we should keep in mind the possibility of an associated organic disorder as the combination of both organic and functional disorders is a relatively frequent situation in clinical practice. Here we describe the clinical characteristics of different functional neurological syndromes: motor deficits, abnormal hyperkinetic and hypokinetic movements, voice or speech disorders, sensory disorders, and functional dissociative seizures. The clinical examination and the identification of positive signs play a critical role in the diagnosis of functional neurological disorder. Knowledge of the specific signs associated with each phenotype render possible to make an early diagnosis. For that matter, it contributes to the improvement of patient care management. It allows to a better engagement in an appropriate care pathway, which influence their prognosis. Highlighting and discussing positive signs with patients can also be an interesting step in the process of explaining the disease and its management.

Differentiating between epileptic and functional/dissociative seizures using semantic content analysis of transcripts of routine clinic consultations

The common causes of Transient Loss of Consciousness (TLOC) are syncope, epilepsy, and functional/dissociative seizures (FDS). Simple, questionnaire-based decision-making tools for non-specialists who may have to deal with TLOC (such as clinicians working in primary or emergency care) reliably differentiate between patients who have experienced syncope and those who have had one or more seizures but are more limited in their ability to differentiate between epileptic seizures and FDS. Previous conversation analysis research has demonstrated that qualitative expert analysis of how people talk to clinicians about their seizures can help distinguish between these two TLOC causes. This paper investigates whether automated language analysis - using semantic categories measured by the Linguistic Inquiry and Word Count (LIWC) toolkit - can contribute to the distinction between epilepsy and FDS. Using patient-only talk manually transcribed from recordings of 58 routine doctor-patient clinic interactions, we compared the word frequencies for 21 semantic categories and explored the predictive performance of these categories using 5 different machine learning algorithms. Machine learning algorithms trained using the chosen semantic categories and leave-one-out cross-validation were able to predict the diagnosis with an accuracy of up to 81%. The results of this proof of principle study suggest that the analysis of semantic variables in seizure descriptions could improve clinical decision tools for patients presenting with TLOC.

Coding linguistic elements in clinical interactions: a step-by-step guide for analyzing communication form

Background

The quality of communication between healthcare professionals (HCPs) and patients affects health outcomes. Different coding systems have been developed to unravel the interaction. Most schemes consist of predefined categories that quantify the content of communication (the what). Though the form (the how) of the interaction is equally important, protocols that systematically code variations in form are lacking. Patterns of form and how they may differ between groups therefore remain unnoticed. To fill this gap, we present CLECI, Coding Linguistic Elements in Clinical Interactions, a protocol for the development of a quantitative codebook analyzing communication form in medical interactions.

Methods

Analyzing with a CLECI codebook is a four-step process, i.e. preparation, codebook development, (double-)coding, and analysis and report. Core activities within these phases are research question formulation, data collection, selection of utterances, iterative deductive and inductive category refinement, reliability testing, coding, analysis, and reporting.

Results and conclusion

We present step-by-step instructions for a CLECI analysis and illustrate this process in a case study. We highlight theoretical and practical issues as well as the iterative codebook development which combines theory-based and data-driven coding. Theory-based codes assess how relevant linguistic elements occur in natural interactions, whereas codes derived from the data accommodate linguistic elements to real-life interactions and contribute to theory-building. This combined approach increases research validity, enhances theory, and adjusts to fit naturally occurring data. CLECI will facilitate the study of communication form in clinical interactions and other institutional settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01647-0.

Differential diagnosis between epileptic and psychogenic nonepileptic seizures through conversational analysis: A blinded prospective study in the Russian language

The current study examined the validity of conversational analysis (CA) in Russian patients with seizures, using a scoring table for the Simplified Linguistic Evaluation (SLE). The study sample was composed of 12 adult participants suffering either from epilepsy (ES) or psychogenic nonepileptic seizures (PNES) recruited in the Moscow Research and Clinical Center for Neuropsychiatry. Definitive diagnosis was established only after a habitual event was captured onvEEG. All participants with PNES or ES and at least one mental disorder underwent a 20-minute-long interview recorded on video. The interview then was evaluated by the external blinded physician already experienced in CA. Finally, that physician filled the SLE, consisting of 5 items analyzing the main characteristics of patient narrations. A score of ≥12 suggested a diagnosis of ES, while a score of <12 suggested a diagnosis of PNES. The blinded evaluator correctly identified 11 out of 12 cases. The concordance between the vEEG diagnosis and the CA diagnostic hypothesis was 91.67%. The sensitivity of the scoring table was 100%, while the specificity was 80%. The positive and the negative predictive values were, respectively, 87.5% and 100%. Our results suggested that the differences in seizure descriptions between patients with PNES and patients with ES are similar across Indo-European language family and are independent of psychiatric comorbidity.

Unexplained versus explained symptoms: The difference is not in patients' language use. A quantitative analysis of linguistic markers

OBJECTIVE

Patients with medically unexplained symptoms (MUS) are believed to have a deviant way of talking about complaints. This study systematically compared linguistic markers in symptom presentations of patients with MUS and medically explained symptoms (MES).

METHODS

This content analysis (cross-sectional study) conceptualized relevant linguistic markers based on previous research about MUS communication. Linguistic markers included negations ("not"), intensifiers ("very"), diminishers ("a little"), first or third person subject ("I" vs. "my body"), subjectivity markers ("I think") and abstraction ("I'm gasping for breath" vs. "I'm short of breath"). We also coded valence, reference to physical or mental states, and consultation phase. We compared 41 MUS and 41 MES transcribed video-recorded general practice consultations. Data were analyzed with binary random intercepts models.

RESULTS

We selected and coded 2752 relevant utterances. Patients with MUS used less diminishers compared to patients with MES, but this main effect disappeared when consultation phase was included as predictor. For all other linguistic variables, the analyses did not reveal any variation in language use based on whether patients had MUS or MES. Importantly, utterances' valence and reference to physical or mental state did predict the use of linguistic markers.

CONCLUSION

We observed no systematic variations in linguistic markers for patients who suffered from MUS compared to MES. Patients varied their language use based on utterances' valence and reference to physical or mental states. Current ideas about deviant patient communication may be based on stigmatized perceptions of how patients with MUS communicate, rather than actual differences in their talk.

Psychogenic non-epileptic seizures (PNES) in the context of concurrent epilepsy – making the right diagnosis

Epilepsy is a risk factor for the development of psychogenic non-epileptic seizures (PNES) and comorbid epilepsy is recognized as a comorbidity in about 10–30% of patients with PNES. The combination of epileptic and nonepileptic seizures poses a particular diagnostic challenge. In patients with epilepsy, additional PNES may be suspected on the basis of their typical semiology. The possibility of additional PNES should also be considered if seizures fail to respond to antiepileptic drug treatment, in patients with frequent emergency admissions with seizures and in those who develop new types of seizures. The description of semiological details by patients and witnesses can suggest additional PNES. Home video recordings can support an initial diagnosis, however, especially in patients with mixed seizure disorders it is advisable to seek further diagnostic confirmation by capturing all habitual seizure types with video-EEG. The clinical features of PNES associated with epilepsy are similar to those in isolated PNES disorders and include longer duration, fluctuating course, asynchronous movements, pelvic thrusting, side-to-side head or body movement, persistently closed eyes and mouth, ictal crying, recall of ictal experiences and absence of postictal confusion. PNES can also present as syncope-like episodes with unresponsiveness and reduced muscle tone. There is no unique epileptological or brain pathology profile putting patients with epilepsy at risk of additional PNES. However, patients with epilepsy and PNES typically have lower educational achievements and higher levels of psychiatric comorbidities than patients with epilepsy alone. Psychological trauma, including sexual abuse, appears to be a less relevant aetiological factor in patients with mixed seizure disorders than those with isolated PNES, and the gender imbalance (i.e. the greater prevalence in women) is less marked in patients with PNES and additional epilepsy than those with PNES alone. PNES sometimes develop after epilepsy surgery. A diagnosis of ‘known epilepsy’ should never be accepted without (at least brief) critical review. This narrative review summarises clinical, electrophysiological and historical features that can help identify patients with epilepsy and additional PNES.

What do we know about non-epileptic seizures in adults with intellectual disability: A narrative review

Psychogenic non-epileptic seizures (PNES) superficially resemble epileptic seizures, but are not associated with abnormal electrical activity in the brain. PNES are a heterogeneous entity and while there is increasing interest in the characterisation of PNES sub-groups, little is known about individuals with PNES who have an intellectual disability (ID). ID is a lifelong condition characterised by significant limitations in cognitive, social and practical skills. ID (commonly with comorbid epilepsy) has been identified as a risk factor for developing PNES. However, people with ID are often excluded from research in PNES. This has unfortunately resulted in a lack of evidence to help inform practice and policy for this population. This narrative review synthesises the currently available evidence in terms of the epidemiology, demographic and clinical profile of adults with PNES and ID. There is a particular focus on demographics, aetiological factors, PNES characteristics, diagnosis and treatment of the condition in this population. Throughout this article, we critique the existing evidence, discuss implications for clinical practice and highlight the need for further research and enquiry. What emerges from the evidence is that, even within the sub-group of those with ID, PNES are a heterogeneous condition. Individuals with ID and PNES are likely to present with diverse and complex needs requiring multidisciplinary care. This review is aimed at the broad range of healthcare professionals who may encounter this group. We hope that it will stimulate further discussion and research initiatives.

Feasibility of using an automated analysis of formulation effort in patients' spoken seizure descriptions in the differential diagnosis of epileptic and nonepileptic seizures

OBJECTIVE

There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished relatively easily with a small number of "yes"/"no" questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that the descriptions of epileptic seizures contain more formulation effort than accounts of PNES. This research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures.

METHOD

Verbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of 45 interactions (21 epilepsy and 24 PNES). The subsection of each transcript containing the person's account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier.

RESULT

There were significantly more hesitations and repetitions in descriptions of epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier.

DISCUSSION

This pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool and compare the performance of these features when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.

Conversational analysis of consciousness during seizures

OBJECTIVE

The objectives of the study were to 1) investigate how patients with epilepsy describe the subjective, conscious experience of having a seizure and 2) determine whether certain themes and descriptions correspond to specific types of epilepsy.

METHODS

We interviewed thirteen patients with electroencephalographically confirmed epilepsy about their subjective experience of having a seizure and used conversational analysis (CA) to analyze the language they used to describe this experience.

RESULTS

Seven patients had focal to bilateral tonic-clonic seizures (FBTCS), 7 had focal impaired awareness seizures (FIAS), 1 had focal aware seizures (FAS), and one had generalized onset tonic-clonic (GTC) seizures. Three had multiple types of seizures. Focal seizure origin was frontal in 2 patients, right hemisphere in 1, parieto-occipital in 1, and temporal in 8. Focal to bilateral tonic-clonic and GTC seizures were most frequently associated with descriptions of a total loss of consciousness (n = 8), whereas FIAS were most frequently associated with a perceived loss of consciousness but able to describe some aspects of being unconscious (n = 5). Temporal seizures most frequently accompanied reports of memory loss/impairment (n = 4). Ten patients provided specific descriptions of the transition between the interictal and ictal state or auras. Descriptions consciousness and unconsciousness ranged significantly, resembling a continuum rather than corresponding to distinct levels.

CONCLUSION

The subjective experience of consciousness for patients with epilepsy may differ by seizure type and origin. These may reflect different involvement of brain regions involved in producing consciousness and arousal. Conversational analysis and narrative approaches can significantly aid clinicians in the diagnosis and management of epilepsy.

Differentiating PNES from epileptic seizures using conversational analysis on French patients: A prospective blinded study

Psychogenic nonepileptic seizures (PNES) resemble epileptic seizures (ES) but are not caused by the occurrence of excessive cortical neuronal discharge. Previous studies in German-, English-, and Italian-speaking patients showed that patients used a different communicative style to talk about their seizures. They demonstrated that the diagnosis between PNES and ES could be predicted using qualitative assessment and a diagnostic scoring aid (DSA). The objective of our study was to evaluate the contribution of linguistic analysis in the differential diagnosis between ES and PNES in a French patient population. During an extended video-electroencephalogram (video-EEG) monitoring, 13 patients presented PNES and 19 patients with ES. Two neurologists blindly and independently analyzed the interview of each patient. Rater 1 predicted the correct diagnosis in 27 of 32 patients (84%) and Rater 2 in 28 of 32 patients (88%). Interrater reliability of qualitative analysis was satisfactory (k = 0.68, interrater agreement = 84.4%). Using a simplified DSA, Rater 1 and Rater 2 would have correctly diagnosed 88% (28/32 patients) and 91 % (29/32) of the cases, respectively. Our blinded prospective study confirms the diagnostic value of conversational analysis, performed by neurologists, to differentiate PNES from ES in French-speaking patients.

Differentiating psychogenic nonepileptic from epileptic seizures: A mixed-methods, content analysis study

BACKGROUND

Identification of clinical features that might distinguish psychogenic nonepileptic seizures (PNES) from epileptic seizures (ES) is of value for diagnosis, management, and understanding of both conditions. Previous studies have shown that patients' descriptions of their seizures reflect differences in content and delivery. We aimed to compare verbal descriptions of PNES and ES using a mixed-methods approach.

METHODS

We analyzed data from semi-structured interviews in which patients with video-electroencephalography (EEG)-confirmed ES (n = 30) or PNES (n = 10) described their seizures. Two masked raters independently coded the transcripts for relevant psychological categories and discrepancies that were noted and resolved. Additional analyses were conducted using the Linguistic Inquiry and Word Count system. The identified phenomena were descriptively compared, and inferential analyses assessed group differences in frequencies. A logistic regression analysis examined the predictive power of the most distinctive phenomena for diagnosis.

RESULTS

As compared with ES, PNES reported longer seizures, more preseizure negative emotions (e.g., fear), anxiety symptoms (e.g., arousal, hyperventilation), altered vision/olfaction, and automatic behaviors. During seizures, PNES reported more fear, altered breathing, and dissociative phenomena (depersonalization, impaired time perception). Epileptic seizures reported more self-injurious behavior. Postseizure, PNES reported more fear and weeping and ES more amnesia and aches. The predictive power when including these variables was 97.5%. None of the single predictor variables was significant. The few but consistent linguistic differences related to the use of some pronouns and references to family.

CONCLUSIONS

Although no single clinical feature definitively distinguishes PNES from ES, several features may be suggestive of a PNES diagnosis, including longer duration, negative emotion (i.e., fear) throughout the events, preseizure anxiety, ictal dissociation, and postseizure weeping. Fewer reports of ictal self-injury and postseizure amnesia and aches may also indicate the possibility of PNES.

The first-line management of psychogenic non-epileptic seizures (PNES) in adults in the emergency: a practical approach

Distinguishing non-epileptic events, especially psychogenic non-epileptic seizures (PNES), from epileptic seizures (ES) constitutes a diagnostic challenge. Misdiagnoses are frequent, especially when video-EEG recording, the gold-standard for PNES confirmation, cannot be completed. The issue is further complicated in cases of combined PNES with ES. In emergency units, a misdiagnosis can lead to extreme antiepileptic drug escalade, unnecessary resuscitation measures (intubation, catheterization, etc.), as well as needless biologic and imaging investigations. Outside of the acute window, an incorrect diagnosis can lead to prolonged hospitalization or increase of unhelpful antiepileptic drug therapy. Early recognition is thus desirable to initiate adequate treatment and improve prognosis. Considering experience-based strategies and a thorough review of the literature, we aimed to present the main clinical clues for physicians facing PNES in non-specialized units, before management is transferred to epileptologists and neuropsychiatrists. In such conditions, patient recall or witness-report provide the first orientation for the diagnosis, recognizing that collected information may be inaccurate. Thorough analysis of an event (live or based on home-video) may lead to a clinical diagnosis of PNES with a high confidence level. Indeed, a fluctuating course, crying with gestures of frustration, pelvic thrusting, eye closure during the episode, and the absence of postictal confusion and/or amnesia are highly suggestive of PNES. Moreover, induction and/or inhibition tests of PNES have a good diagnostic value when positive. Prolactinemia may also be a useful biomarker to distinguish PNES from epileptic seizures, especially following bilateral tonic-clonic seizures. Finally, regardless the level of certainty in the diagnosis of the PNES, it is important to subsequently refer the patient for epileptological and neuropsychiatric follow-up.

Linguistic and interactional aspects that characterize consultations about medically unexplained symptoms: A systematic review

OBJECTIVE

The apparent absence of any specific underlying diseases challenges patient-provider communication about medically unexplained symptoms (MUS). Previous research focused on general communication patterns in these interactions; however, an overview of more detailed interactional and linguistic aspects is lacking. This review aims to gain a detailed understanding of communicative challenges in MUS consultations by synthesizing evidence from conversation and discourse analytic research.

METHODS

A systematic review of publications using eight databases (PubMed, Embase, CINAHL, PsychINFO, Web of Science, MLA International Bibliography, LLBA and Communication Abstracts). Search terms included 'MUS', 'linguistics' and 'communication'. Additional studies were identified by contacting experts and searching bibliographies. We included linguistic and/or interactional analyses of natural patient-provider interactions about MUS. Two authors independently extracted the data, and quality appraisal was based on internal and external validity.

RESULTS

We identified 18 publications that met the inclusion criteria. The linguistic and interactional features of MUS consultations pertained to three dimensions: 1) symptom recognition, 2) double trouble potential (i.e. patients and providers may have differing views on symptoms and differing knowledge domains), and 3) negotiation and persuasion (in terms of acceptable explanations and subsequent psychological treatment). We describe the recurrent linguistic and interactional features of these interactions.

CONCLUSIONS

Despite the presence of a double trouble potential in MUS consultations, validation of symptoms and subtle persuasive conduct may facilitate agreement on illness models and subsequent (psychological) treatment.

An Interactional Profile to Assist the Differential Diagnosis of Neurodegenerative and Functional Memory Disorders

OBJECTIVE

Specialist services for dementia are seeing an increasing number of patients. We investigated whether interactional and linguistic features in the communication behavior of patients with memory problems could help distinguish between those with problems secondary to neurological disorders (ND) and those with functional memory disorder (FMD).

METHODS

In part 1 of this study, a diagnostic scoring aid (DSA) was developed encouraging linguists to provide quantitative ratings for 14 interactional features. An optimal cut-off differentiating ND and FMD was established by applying the DSA to 30 initial patient-doctor memory clinic encounters. In part 2, the DSA was tested prospectively in 10 additional cases analyzed independently by 2 conversation analysts blinded to medical information.

RESULTS

In part 1, the median score of the DSA was +5 in ND and -5 in FMD (P<0.001). The optimal numeric DSA cut-off (+1) identified patients with ND with a sensitivity of 86.7% and a specificity of 100%. In part 2, DSA scores of rater 1 correctly predicted 10/10 and those of rater 2 predicted 9/10 diagnoses.

CONCLUSIONS

This study indicates that interactional and linguistic features can help distinguish between patients developing dementia and those with FMD and could aid the stratification of patients with memory problems.

CRFVoter: gene and protein related object recognition using a conglomerate of CRF-based tools

BACKGROUND

Gene and protein related objects are an important class of entities in biomedical research, whose identification and extraction from scientific articles is attracting increasing interest. In this work, we describe an approach to the BioCreative V.5 challenge regarding the recognition and classification of gene and protein related objects. For this purpose, we transform the task as posed by BioCreative V.5 into a sequence labeling problem. We present a series of sequence labeling systems that we used and adapted in our experiments for solving this task. Our experiments show how to optimize the hyperparameters of the classifiers involved. To this end, we utilize various algorithms for hyperparameter optimization. Finally, we present CRFVoter, a two-stage application of Conditional Random Field (CRF) that integrates the optimized sequence labelers from our study into one ensemble classifier.

RESULTS

We analyze the impact of hyperparameter optimization regarding named entity recognition in biomedical research and show that this optimization results in a performance increase of up to 60%. In our evaluation, our ensemble classifier based on multiple sequence labelers, called CRFVoter, outperforms each individual extractor's performance. For the blinded test set provided by the BioCreative organizers, CRFVoter achieves an F-score of 75%, a recall of 71% and a precision of 80%. For the GPRO type 1 evaluation, CRFVoter achieves an F-Score of 73%, a recall of 70% and achieved the best precision (77%) among all task participants.

CONCLUSION

CRFVoter is effective when multiple sequence labeling systems are to be used and performs better then the individual systems collected by it.

Value of witness observations in the differential diagnosis of transient loss of consciousness

OBJECTIVE

This retrospective study explores to what extent additional information from event witnesses provided using the novel 31-item Paroxysmal Event Observer (PEO) Questionnaire improves the differentiation among epilepsy, syncope, and psychogenic nonepileptic seizures (PNES) achievable with information provided by patients alone.

METHODS

Patients with transient loss of consciousness caused by proven epilepsy (n = 86), syncope (n = 79), or PNES (n = 84) attending specialist neurology/syncope services in the United Kingdom and event observers provided Paroxysmal Event Profile (PEP), PEO, and personal information (PI) (e.g., sex, age, medical history) data. PEO data were subjected to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). PEO, PEP, and PI data were used separately and in combination to differentiate diagnoses by pairwise and multinomial logistic regressions. Predicted diagnoses were compared with gold standard medical diagnoses.

RESULTS

EFA/CFA identified a 4-factor structure of the PEO based on 26/31 questionnaire items with loadings ≥0.4. Observer-reported factors alone differentiated better between syncope and epilepsy than patient-reported factors (accuracy: 96% vs 85%, p = 0.0004). Observer-reported data improved accuracy over differentiation based on patient-reported data alone from 90% to 100% between syncope and epilepsy (p = 0.005), 76% to 83% between epilepsy and PNES (p = 0.006), and 93% to 95% between syncope and PNES (p = 0.098).

CONCLUSIONS

Information from observers can make an important contribution to the differentiation of epilepsy from syncope or PNES but adds less to that of syncope from PNES.

A staff training intervention to improve communication between people living with dementia and health-care professionals in hospital: the VOICE mixed-methods development and evaluation study

Background

Twenty-five per cent of hospital beds are occupied by a person living with dementia. Dementia affects expressive communication and understanding. Health-care professionals report a lack of communication skills training.

Objectives

To identify teachable, effective strategies for communication between health-care professionals and people living with dementia, and to develop and evaluate a communication skills training course.

Design

We undertook a systematic literature review, video-recorded 41 encounters between staff and people with dementia, and used conversation analysis to investigate communication problems and solutions. We designed a communication skills training course using coproduction and multiple pedagogic approaches. We ran a pilot, followed by six courses for health-care professionals. We measured knowledge, confidence and communication behaviours before, immediately after and 1 month after the course, and undertook interviews with participants and managers. Behaviours were measured using blind-rated videos of simulations.

Setting

General hospital acute geriatric medical wards and two hospital clinical skills centres.

Participants

We video-recorded 26 people with dementia and 26 professionals. Ten experts in dementia care, education, simulation and communication contributed to intervention development. Six health-care professionals took part in a pilot course, and 45 took part in the training.

Results

The literature review identified 26 studies describing 10 communication strategies, with modest evidence of effectiveness. Health-care professional-initiated encounters followed a predictable phase structure. Problems were apparent in requests (with frequent refusals) and in closings. Success was more likely when requests were made directly, with high entitlement (authority to ask) and with lowered contingencies (made to sound less difficult, by minimising the extent or duration of the task, asking patients ‘to try’, offering help or proposing collaborative action). Closings were more successful if the health-care professional announced the end of the task, made a specific arrangement, body language matched talk, and through use of ‘closing idioms’. The training course comprised 2 days, 1 month apart, using experiential learning, including lectures, video workshops, small group discussions, simulations (with specially trained actors) and reflections. We emphasised the incorporation of previous expertise and commitment to person-centred care. Forty-four participants returned for the second training day and 43 provided complete evaluation data. Knowledge and confidence both increased. Some behaviours, especially relating to closings, were more commonly used after training. The course was rated highly in interviews, especially the use of simulations, real-life video clips and interdisciplinary learning. Participants reported that they found the methods useful in practice and were using them 1 month after the course finished.

Limitations

Data were from people with moderate to severe dementia, in an acute hospital, during health-care professional-initiated interactions. Analysis was limited to problems and solutions that were likely to be ‘trainable’. Actors required careful preparation to simulate people with dementia. Communication skills training course participants were volunteers, unlikely to be representative of the general workforce, who displayed high levels of baseline knowledge, confidence and skills. Before-and-after evaluations, and qualitative interviews, are prone to bias.

Conclusions

Requests and closings pose particular difficulties for professionals communicating with people with dementia. We identified solutions to these problems and incorporated them into communication skills training, which improved knowledge, confidence and some communication behaviours. Simulation was an effective training modality.

Future work

Further research should investigate a wider range of health, social care and family carers. Conversation analysis should be used to investigate other aspects of health-care communication.

Study registration

The systematic literature review is registered as CRD42015023437.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Developing an intelligent virtual agent to stratify people with cognitive complaints: A comparison of human–patient and intelligent virtual agent–patient interaction

Previous work on interactions in the memory clinic has shown that conversation analysis can be used to differentiate neurodegenerative dementia from functional memory disorder. Based on this work, a screening system was developed that uses a computerised ‘talking head’ (intelligent virtual agent) and a combination of automatic speech recognition and conversation analysis-informed programming. This system can reliably differentiate patients with functional memory disorder from those with neurodegenerative dementia by analysing the way they respond to questions from either a human doctor or the intelligent virtual agent. However, much of this computerised analysis has relied on simplistic, nonlinguistic phonetic features such as the length of pauses between talk by the two parties.

To gain confidence in automation of the stratification procedure, this paper investigates whether the patients’ responses to questions asked by the intelligent virtual agent are qualitatively similar to those given in response to a doctor. All the participants in this study have a clear functional memory disorder or neurodegenerative dementia diagnosis.

Analyses of patients’ responses to the intelligent virtual agent showed similar, diagnostically relevant sequential features to those found in responses to doctors’ questions. However, since the intelligent virtual agent’s questions are invariant, its use results in more consistent responses across people – regardless of diagnosis – which facilitates automatic speech recognition and makes it easier for a machine to learn patterns. Our analysis also shows why doctors do not always ask the same question in the exact same way to different patients. This sensitivity and adaptation to nuances of conversation may be interactionally helpful; for instance, altering a question may make it easier for patients to understand. While we demonstrate that some of what is said in such interactions is bound to be constructed collaboratively between doctor and patient, doctors could consider ensuring that certain, particularly important and/or relevant questions are asked in as invariant a form as possible to be better able to identify diagnostically relevant differences in patients’ responses.

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints

BACKGROUND

The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD).

OBJECTIVE

This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated.

METHODS

Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD.

RESULTS

The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%.

CONCLUSION

This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD.

Psychogenic non-epileptic seizures: aetiology, diagnosis and management

Psychogenic non-epileptic seizures (PNES) have a significant impact on most patients in terms of distress, disability, loss of income and iatrogenic harm. Three-quarters of patients with PNES are initially misdiagnosed and treated for epilepsy. Misdiagnosis exposes patients to multiple iatrogenic harms and prevents them from accessing psychological treatment. In most cases, the patient's history (and witness accounts) should alert clinicians to the likely diagnosis of PNES. Since this diagnosis may be resisted by patients and may involve ‘un-diagnosing’ epilepsy, video-electroencephalogram recording of typical seizures is often helpful. The underlying causes of PNES are diverse: a model combining predisposing, precipitating and perpetuating factors is a useful way of conceptualising their aetiology. The initial step of treatment should be to limit iatrogenic harm. There is some evidence for the effectiveness of psychotherapy.

Differentiating PNES from epileptic seizures using conversational analysis

We applied conversation analysis in an unselected continuous series of 70 patients to discriminate patients with psychogenic nonepileptic seizures (PNES) from patients with epilepsy. Two psychologists examined the patients' recorded reports. Patients were also submitted to an extensive neuropsychological battery in order to verify whether specific cognitive deficits or mental health problems are typical of patients with PNES and whether some cognitive deficits could prevent the correct diagnosis. The results showed a good percentage of correct diagnosis, with a sensitivity of 0.795 and a specificity of 0.83, while no difference in the cognitive profile was found between patients with PNES and patients with epilepsy. The results also suggest that psychologists can apply the conversation analysis as well as linguists, which is an important finding since psychologists are employed in specialized centers, while linguists in general are not part of the team.

Neurologists can identify diagnostic linguistic features during routine seizure clinic interactions: results of a one-day teaching intervention

The diagnostic distinction between epilepsy and psychogenic nonepileptic seizures (PNES) can be challenging. Previous studies have demonstrated that experts in conversation analysis can identify linguistic and interactional features in transcripts and recordings of interviews with patients that reliably distinguish between epilepsy and PNES. In this study, ten senior neurology trainees took part in a one-day intervention workshop about linguistic and interactional differences in the conversation behavior of patients with epilepsy and those with PNES. Participants were familiarized with a 12-item questionnaire designed to capture their conversational observations immediately after talking to a patient with seizures. After the intervention, 55 initial outpatient visits of patients referred to seizure clinics were video and audio recorded. All medical diagnoses were confirmed two years after initial presentation on the basis of a chart review (including MRI and EEG findings) by a fully trained epilepsy expert. Postvisit questionnaires relating to patients confirmed to have epilepsy (n=20) or PNES (n=13) were analyzed. Doctors' mean responses to 6 of the 12 questions about linguistic and interactional observations differed significantly between the groups with epilepsy and PNES. Receiver operating curve analysis showed that a summation scale based on items demonstrating significant between-group differences correctly classified 81.8% of patients as having epilepsy or PNES. This study shows that a brief Conversation Analytic teaching intervention can enable neurologists to identify linguistic and interactional features supporting the differentiation of epilepsy and PNES as they take their patients' history in routine seizure clinic consultations, potentially improving diagnostic accuracy.

Systemizing and empathy in forensic ASD talk

Purpose

Since often missed in forensic care settings, little is known about how the autism spectrum disorder (ASD) social and communication deficit impacts on rehabilitation work, particularly when accompanied by intellectual disability. The purpose of this paper is to show how Baron-Cohen’s empathizing – systemizing theory can elucidate common processes in the interaction-based risk-reduction work carried on between ASD forensic patients and their clinicians.

Design/methodology/approach

Conversation analysis (CA) is used to analyse the talk of two ASD men engaged in risk reduction work with their clinicians on a forensic intellectual disability ward in a medium secure psychiatric hospital in the UK. The clinicians include two forensic nurses and a speech and language therapist.

Findings

Clinicians adapt to their patients’ systematic processes particularly with regard to helping them understand complex social phenomena such as others’ emotional displays and their understanding of empathy.

Practical implications

Since ASD in forensic care is poorly researched, clinicians have little in the way of guidance about the interactive strengths and weaknesses of their ASD patients, despite risk reduction work being carried out by means of conversational interactions. This paper demonstrates some key aspects of ASD clinical interactions which may be used to inform treatment strategies elsewhere in the forensic establishment.

Originality/value

By using Baron-Cohen’s empathizing systemizing theory in combination with CA, this paper aims to bring understanding of ASD interaction up to date. This is of particular importance for this poorly researched patient group, who, because of the way in which they differ to standard psychiatric patients, are at risk of being detained for lengthy periods where treatment strategies are not designed to fit their social and communicative profiles.

Conversation analysis in the differentiation of psychogenic nonepileptic and epileptic seizures in pediatric and adolescent settings

The differential diagnosis of epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is often difficult, especially in pediatric and adolescent settings. Conversation analysis (CA) can be a worthwhile diagnostic tool in adults. The aim of this study was to assess the diagnostic value of CA in Italian children and adolescents. Ten patients (seven females and three males), diagnosed using video-EEG as having either ES or PNES, underwent a video-recorded interview by a physician from outside the center specifically trained for this purpose. An external linguistic rater then examined the video recordings and transcripts using CA. Diagnoses formulated on the basis of interactional and linguistic features of the patients' speech were compared with diagnoses made by seizure experts on the basis of all available clinical information including the video-EEG findings. Conversation analysis diagnoses corresponded to the video-EEG diagnoses in 8 out of 10 cases. In conclusion, while some conversational adaptation is necessary to enable children and adolescents to share their seizure experiences with an adult health professional, this study indicates the differential diagnostic potential of a CA approach in these young people with PNES or epilepsy. Larger samples are obviously needed to confirm these findings.

Conversational assessment in memory clinic encounters: interactional profiling for differentiating dementia from functional memory disorders

OBJECTIVES

In the UK dementia is under-diagnosed, there is limited access to specialist memory clinics, and many of the patients referred to such clinics are ultimately found to have functional (non-progressive) memory disorders (FMD), rather than a neurodegenerative disorder. Government initiatives on 'timely diagnosis' aim to improve the rate and quality of diagnosis for those with dementia. This study seeks to improve the screening and diagnostic process by analysing communication between clinicians and patients during initial specialist clinic visits. Establishing differential conversational profiles could help the timely differential diagnosis of memory complaints.

METHOD

This study is based on video- and audio recordings of 25 initial consultations between neurologists and patients referred to a UK memory clinic. Conversation analysis was used to explore recurrent communicative practices associated with each diagnostic group.

RESULTS

Two discrete conversational profiles began to emerge, to help differentiate between patients with dementia and functional memory complaints, based on (1) whether the patient is able to answer questions about personal information; (2) whether they can display working memory in interaction; (3) whether they are able to respond to compound questions; (4) the time taken to respond to questions; and (5) the level of detail they offer when providing an account of their memory failure experiences.

CONCLUSION

The distinctive conversational profiles observed in patients with functional memory complaints on the one hand and neurodegenerative memory conditions on the other suggest that conversational profiling can support the differential diagnosis of functional and neurodegenerative memory disorders.

Value of patient-reported symptoms in the diagnosis of transient loss of consciousness

OBJECTIVE

Epileptic seizures, syncope, and psychogenic nonepileptic seizures (PNES) account for over 90% of presentations with transient loss of consciousness (TLOC). The patient's history is crucial for the diagnosis, but the diagnostic value of individual semiologic features is limited. This study explores the diagnostic potential of a comprehensive questionnaire focusing on TLOC-associated symptoms.

METHODS

A total of 386 patients with proven epilepsy, 308 patients with proven PNES, and 371 patients with proven syncope were approached by post to recruit 100 patients in each diagnostic group. Symptoms were self-reported on an 86-item questionnaire (the Paroxysmal Event Profile [PEP]) using a 5-point Likert scale (always to never). Data were subjected to exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA). Factors were used to differentiate between diagnoses by pairwise and multinomial regression.

RESULTS

Patients with PNES reported more and more frequent TLOC-associated symptoms than those with epilepsy or syncope (p < 0.001). EFA/CFA identified a 5-factor structure based on 74/86 questionnaire items with loadings ≥0.4. Pairwise logistic regression analysis correctly classified 91% of patients with epilepsy vs those with syncope, 94% of those with PNES vs those with syncope, and 77% of those with epilepsy vs those with PNES. Multinomial logistic regression analysis yielded a similar pattern.

CONCLUSIONS

Clusters of self-reported TLOC symptoms can be used to direct patients to appropriate investigation and treatment pathways for syncope on the one hand and seizures on the other, although additional information is required for a reliable distinction, especially between epilepsy and PNES.

The role of companions in outpatient seizure clinic interactions: A pilot study

PURPOSE

This study explored contributions that patients' companions (seizure witnesses) make to interactions in the seizure clinic and whether the nature of the companions' interactional contributions can help with the differentiation of epilepsy and psychogenic nonepileptic seizures (PNES).

METHODS

Conversation analysis methods were used to examine video recordings and transcripts of neurologists' interactions with patients referred to a specialist seizure clinic and their companions.

RESULTS

The companions' behavior correlated with interactional features previously observed to distinguish patients with epilepsy from patients with PNES. Patients with PNES, but not those with epilepsy, tended to exhibit interactional resistance to the doctor's efforts to find out more about their seizure experiences and, thereby, encouraged greater interactional contribution from companions.

CONCLUSION

The contributions that companions make (in part, prompted by patient's interactional behavior) may provide additional diagnostic pointers in this clinical setting, and a number of candidate features that may help clinicians distinguish between epilepsy and PNES when the patient is accompanied by a seizure witness are described. However, companion contributions may limit the doctor's ability to identify linguistic and interactional features with previously demonstrated diagnostic potential in the conversational contributions made by patients themselves. To help offset potential diagnostic losses, doctors may need to explicitly discuss the role of the companion in the consultation when a seizure witness (or another companion) accompanies the patient.

Nonepileptic seizures - subjective phenomena

Psychogenic nonepileptic seizures (PNES) superficially resemble epileptic seizures or syncope and most patients with PNES are initially misdiagnosed as having one of the latter two types of transient loss of consciousness. However, evidence suggests that the subjective seizure experience of PNES and its main differential diagnoses are as different as the causes of these three disorders. In spite of this, and regardless of the fact that PNES are considered a mental disorder in the current nosologies, research has only given limited attention to the subjective symptomatology of PNES. Instead, most phenomenologic research has focused on the visible manifestations of PNES and on physiologic parameters, neglecting patients' symptoms and experiences. This chapter gives an overview of qualitative and quantitative studies providing insights into subjective symptoms associated with PNES, drawing on a wide range of methodologies (questionnaires, self-reports, physiologic measures, linguistic analyses, and neuropsychologic experiments). After discussing the scope and limitations of these approaches in the context of this dissociative phenomenon, we discuss ictal, peri-ictal and interictal symptoms described by patients with PNES. We particularly focus on impairment of consciousness. PNES emerges as a clinically heterogeneous condition. We conclude with a discussion of the clinical significance of particular subjective symptoms for the engagement of patients in treatment, the formulation of treatment, and prognosis.

A brief conversation analytic communication intervention can change history-taking in the seizure clinic

Question design during history-taking has clear implications for patients' ability to share their concerns in general and their seizure experiences in particular. Studies have shown that unusually open questions at the start of the consultation enable patients to display interactional and linguistic markers which may help with the otherwise challenging differentiation of epileptic from nonepileptic seizures (NES). In this study, we compared the problem presentation approach taken by trainee neurologists in outpatient encounters with new patients before and after a one-day conversation analytic training intervention in which doctors were taught to adopt an open format of question design and recognize diagnostically relevant linguistic features. We audio/video-recorded clinical encounters between ten doctors, their patients, and accompanying persons; transcribed the interactions; and carried out quantitative and qualitative analyses. We studied 39 encounters before and 55 after the intervention. Following the intervention, doctors were significantly more likely to use nondirective approaches to soliciting patient accounts of their presenting complaints that invited the patient to describe their problems from their own point of view and gave them better opportunity to determine the initial agenda of the encounter. The time to first interruption by the doctor increased (from 52 to 116 s, p<.001). While patients were given more time to describe their seizure experiences, the overall appointment length did not increase significantly (19 vs 21 min, n.s.). These changes gave patients more conversational space to express their concerns and, potentially, to demonstrate the interactional and linguistic features previously found to help differentiate between epilepsy and NES, without impacting the length of the consultations.

Towards diagnostic conversational profiles of patients presenting with dementia or functional memory disorders to memory clinics

OBJECTIVE

This study explores whether the profile of patients' interactional behaviour in memory clinic conversations with a doctor can contribute to the clinical differentiation between functional memory disorders (FMD) and memory problems related to neurodegenerative diseases.

METHODS

Conversation Analysis of video recordings of neurologists' interactions with patients attending a specialist memory clinic. "Gold standard" diagnoses were made independently of CA findings by a multi-disciplinary team based on clinical assessment, neuropsychological testing and brain imaging.

RESULTS

Two discrete conversational profiles for patients with memory complaints emerged, including (i) who attends the clinic (i.e., whether or not patients are accompanied), and (ii) patients' responses to neurologists' questions about memory problems, such as difficulties with compound questions and providing specific and elaborated examples and frequent "I don't know" responses.

CONCLUSION

Specific communicative difficulties are characteristic of the interaction patterns of patients with a neurodegenerative pathology. Those difficulties are manifest in memory clinic interactions with neurologists, thereby helping to differentiate patients with dementia from those with FMD.

PRACTICAL IMPLICATIONS

Our findings demonstrate that conversational profiles based on patients' contributions to memory clinic encounters have diagnostic potential to assist the screening and referral process from primary care, and the diagnostic service in secondary care.

Seizure metaphors in children with epilepsy: A study based on a multiple-choice self-report questionnaire

The advantages of metaphorical representation are pointed out in many fields of clinical research (e.g. cancer, HIV, psychogenic nonepileptic seizures). This study aimed at offering a novel contribution showing how children with epilepsy describe the symptomatology of their seizure experiences by means of particular kinds of cognitive metaphors. Twenty-three children with idiopathic generalized epilepsy and thirty-one healthy children were recruited for this study and interviewed with a multiple-choice questionnaire asking them to describe their epileptic seizures by means of suitable metaphors. A psychologist blinded to medical diagnosis assessed and categorized all metaphors. By considering the 89 metaphors produced by the children with epilepsy and the 147 ones by the healthy controls, Agent/Force was the primary metaphor assessed by children with epilepsy, followed by Event/Situation as the second preference. Moreover, comparing the results of the control group with those of the subjects with epilepsy, it was found that controls were oriented towards selecting exogenous forces, while subjects with epilepsy tended to select endogenous forces. In particular, children with epilepsy showed a peculiar preference for an endogenous force resembling the waggle metaphor, which is similar to the effect of a quake's shaking (earthquake or seaquake). The metaphors identified by this research are a useful resource to better understand the seizure experiences of patients with epilepsy, helping to improve clinical treatment.

Interpreter-mediated dentistry

The global movements of healthcare professionals and patient populations have increased the complexities of medical interactions at the point of service. This study examines interpreter mediated talk in cross-cultural general dentistry in Hong Kong where assisting para-professionals, in this case bilingual or multilingual Dental Surgery Assistants (DSAs), perform the dual capabilities of clinical assistant and interpreter. An initial language use survey was conducted with Polyclinic DSAs (n = 41) using a logbook approach to provide self-report data on language use in clinics. Frequencies of mean scores using a 10-point visual analogue scale (VAS) indicated that the majority of DSAs spoke mainly Cantonese in clinics and interpreted for postgraduates and professors. Conversation Analysis (CA) examined recipient design across a corpus (n = 23) of video-recorded review consultations between non-Cantonese speaking expatriate dentists and their Cantonese L1 patients. Three patterns of mediated interpreting indicated were: dentist designated expansions; dentist initiated interpretations; and assistant initiated interpretations to both the dentist and patient. The third, rather than being perceived as negative, was found to be framed either in response to patient difficulties or within the specific task routines of general dentistry. The findings illustrate trends in dentistry towards personalized care and patient empowerment as a reaction to product delivery approaches to patient management. Implications are indicated for both treatment adherence and the education of dental professionals.

Automated differentiation between epileptic and nonepileptic convulsive seizures

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.

Psychogenic non-epileptic seizures: so-called psychiatric comorbidity and underlying defense mechanisms

In Diagnostic and Statistical Manual of Mental Disorders, fifth edition, psychogenic non-epileptic seizures (PNES) do not have a unique classification as they can be found within different categories: conversion, dissociative, and somatization disorders. The ICD-10, instead, considers PNES within dissociative disorders, merging the dissociative disorders and conversion disorders, although the underlying defense mechanisms are different. The literature data show that PNES are associated with cluster B (mainly borderline) personality disorders and/or to people with depressive or anxiety disorders. Defense mechanisms in patients with PNES with a prevalence of anxious/depressive symptoms are of "neurotic" type; their goal is to lead to a "split", either vertical (dissociation) or horizontal (repression). The majority of patients with this type of PNES have alexithymia traits, meaning that they had difficulties in feeling or perceiving emotions. In subjects where PNES are associated with a borderline personality, in which the symbolic function is lost, the defense mechanisms are of a more archaic nature (denial). PNES with different underlying defense mechanisms have different prognoses (despite similar severity of PNES) and need usually a different treatment (pharmacological or psychological). Thus, it appears superfluous to talk about psychiatric comorbidity, since PNES are a different symptomatic expression of specific psychiatric disorders.

Anxiety and avoidance in psychogenic nonepileptic seizures: the role of implicit and explicit anxiety

This study examined implicit and explicit anxiety in individuals with epilepsy and psychogenic nonepileptic seizures (PNESs) and explored whether these constructs were related to experiential avoidance and seizure frequency. Based on recent psychological models of PNESs, it was hypothesized that nonepileptic seizures would be associated with implicit and explicit anxiety and experiential avoidance. Explicit anxiety was measured by the State-Trait Anxiety Inventory; implicit anxiety was measured by an Implicit Relational Assessment Procedure; and experiential avoidance was measured with the Multidimensional Experiential Avoidance Questionnaire. Although both groups with epilepsy and PNESs scored similarly on implicit measures of anxiety, significant implicit-explicit anxiety discrepancies were only identified in patients with PNESs (p<.001). In the group with PNESs (but not in the group with epilepsy), explicit anxiety correlated with experiential avoidance (r=.63, p<.01) and frequency of seizures (r=.67, p<.01); implicit anxiety correlated with frequency of seizures only (r=.56, p<.01). Our findings demonstrate the role of implicit anxiety in PNESs and provide additional support for the contribution of explicit anxiety and experiential avoidance to this disorder.

Dissociative symptoms and epilepsy

This article considers the relationship between various types of dissociative symptoms, including symptoms of depersonalization, derealization, and conversion disorders, and epilepsy. After introductory remarks concerning dissociation, this relationship is discussed through two main themes: firstly, the phenomenology and mechanisms of so-called 'dreamy states' in epilepsy and their closest analogs in psychiatric disorders, and secondly, the similarities and differences between epileptic seizures and psychogenic nonepileptic attacks. Although epileptic and dissociative symptoms may appear similar to observers, they arise through different mechanisms and have different experiential qualities.