Optimal filters for ERP research I: A general approach for selecting filter settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Optimal filters for ERP research II: Recommended settings for seven common ERP components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring methods, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for data sets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.

Temporal discounting in children and adolescents with and without attention-deficit/hyperactivity disorder: a comparison of four scoring methods

Temporal discounting (TD) tasks measure the preference for immediate rewards over larger delayed rewards and have been widely used to study impulsivity in children and adolescents with Attention-Deficit/Hyperactivity Disorder (ADHD). Relatively impulsive individuals tend to show high inconsistency in their choices, which makes it difficult to determine commonly used TD outcome measures (e.g., area under the curve, AUC). In this study, we leveraged two published datasets to compare four methods to compute TD outcome measures in children and adolescents (8-17 years) with (n = 107) and without ADHD (n = 128): two predetermined rules methods, a proportion method, and logistic regression. In both datasets, when using the two predetermined rules methods and the proportion method, TD outcomes were highly correlated and group differences in TD were similar. When using logistic regression, a large proportion of AUCs (95% in dataset 1; 33% in dataset 2) could not be computed due to inconsistent choice patterns. These findings indicate that predetermined rules methods (for studies with small sample sizes and experienced raters) and a proportion method (for studies with larger sample sizes or less experienced raters) are recommended over logistic regression when determining subjective reward values for participants with inconsistent choice patterns.

A Developmental Surveillance Score for Quantitative Monitoring of Early Childhood Milestone Attainment: Algorithm Development and Validation

BACKGROUND

Developmental surveillance, conducted routinely worldwide, is fundamental for timely identification of children at risk of developmental delays. It is typically executed by assessing age-appropriate milestone attainment and applying clinical judgment during health supervision visits. Unlike developmental screening and evaluation tools, surveillance typically lacks standardized quantitative measures, and consequently, its interpretation is often qualitative and subjective.

OBJECTIVE

Herein, we suggested a novel method for aggregating developmental surveillance assessments into a single score that coherently depicts and monitors child development. We described the procedure for calculating the score and demonstrated its ability to effectively capture known population-level associations. Additionally, we showed that the score can be used to describe longitudinal patterns of development that may facilitate tracking and classifying developmental trajectories of children.

METHODS

We described the Developmental Surveillance Score (DSS), a simple-to-use tool that quantifies the age-dependent severity level of a failure at attaining developmental milestones based on the recently introduced Israeli developmental surveillance program. We evaluated the DSS using a nationwide cohort of >1 million Israeli children from birth to 36 months of age, assessed between July 1, 2014, and September 1, 2021. We measured the score's ability to capture known associations between developmental delays and characteristics of the mother and child. Additionally, we computed series of the DSS in consecutive visits to describe a child's longitudinal development and applied cluster analysis to identify distinct patterns of these developmental trajectories.

RESULTS

The analyzed cohort included 1,130,005 children. The evaluation of the DSS on subpopulations of the cohort, stratified by known risk factors of developmental delays, revealed expected relations between developmental delay and characteristics of the child and mother, including demographics and obstetrics-related variables. On average, the score was worse for preterm children compared to full-term children and for male children compared to female children, and it was correspondingly worse for lower levels of maternal education. The trajectories of scores in 6 consecutive visits were available for 294,000 children. The clustering of these trajectories revealed 3 main types of developmental patterns that are consistent with clinical experience: children who successfully attain milestones, children who initially tend to fail but improve over time, and children whose failures tend to increase over time.

CONCLUSIONS

The suggested score is straightforward to compute in its basic form and can be easily implemented as a web-based tool in its more elaborate form. It highlights known and novel relations between developmental delay and characteristics of the mother and child, demonstrating its potential usefulness for surveillance and research. Additionally, it can monitor the developmental trajectory of a child and characterize it. Future work is needed to calibrate the score vis-a-vis other screening tools, validate it worldwide, and integrate it into the clinical workflow of developmental surveillance.

Variations in ERP data quality across paradigms, participants, and scoring procedures

Although it is widely accepted that data quality for event-related potential (ERP) components varies considerably across studies and across participants within a study, ERP data quality has not received much systematic analysis. The present study used a recently developed metric of ERP data quality- the standardized measurement error (SME)-to examine how data quality varies across different ERP paradigms, across individual participants, and across different procedures for quantifying amplitude and latency values. The EEG recordings were taken from the ERP CORE, which includes data from 40 neurotypical college students for seven widely studied ERP components: P3b, N170, mismatch negativity, N400, error-related negativity, N2pc, and lateralized readiness potential. Large differences in data quality were observed across the different ERP components, and very large differences in data quality were observed across participants. Data quality also varied depending on the algorithm used to quantify the amplitude and especially the latency of a given ERP component. These results provide an initial set of benchmark values that can be used for comparison with previous and future ERP studies. They also provide useful information for predicting effect sizes and statistical power in future studies, even with different numbers of trials. More broadly, this study provides a general approach that could be used to determine which specific experimental designs, data collection procedures, and data processing algorithms lead to the best data quality.

Optimal Filters for ERP Research II: Recommended Settings for Seven Common ERP Components

In research with event-related potentials (ERPs), aggressive filters can substantially improve the signal-to-noise ratio and maximize statistical power, but they can also produce significant waveform distortion. Although this tradeoff has been well documented, the field lacks recommendations for filter cutoffs that quantitatively address both of these competing considerations. To fill this gap, we quantified the effects of a broad range of low-pass filter and high-pass filter cutoffs for seven common ERP components (P3b, N400, N170, N2pc, mismatch negativity, error-related negativity, and lateralized readiness potential) recorded from a set of neurotypical young adults. We also examined four common scoring methods (mean amplitude, peak amplitude, peak latency, and 50% area latency). For each combination of component and scoring method, we quantified the effects of filtering on data quality (noise level and signal-to-noise ratio) and waveform distortion. This led to recommendations for optimal low-pass and high-pass filter cutoffs. We repeated the analyses after adding artificial noise to provide recommendations for datasets with moderately greater noise levels. For researchers who are analyzing data with similar ERP components, noise levels, and participant populations, using the recommended filter settings should lead to improved data quality and statistical power without creating problematic waveform distortion.

Improved assessment of fecal incontinence in women with previous obstetric injury combining Low Anterior Resection Syndrome and Wexner scores

OBJECTIVE

The aim of the current study was to assess whether Low Anterior Resection Syndrome (LARS) score could provide additional unique information to the Wexner score when assessing fecal incontinence (FI) in women with previous obstetric injury, thus providing a better foundation for treatment decisions.

METHODS

This was a retrospective cohort study with intraindividual comparison of two scoring systems. Women with previous obstetric injury and diagnosed with FI between January 1, 2015, and December 31, 2018, with valid LARS and Wexner scores were included. Statistical methods used were Spearman rank correlation, Kendall τ, scatterplot, and ratios.

RESULTS

Seventy women were included. Correlation coefficients varied from 0.42 to 0.66 (Spearman rank correlation) and 0.44 to 0.51 (Kendall τ). Cohen κ values varied from 0.33 to 0.67. No strong association was seen in the correlation analyses or the scatterplot.

CONCLUSION

LARS score was shown to provide extra relevant information when assessing FI in women with previous obstetric injury. All symptoms should be considered relevant when assessing FI since it is a complex condition and should be approached accordingly. The authors suggest a combination of LARS and Wexner scores when assessing FI among women with previous obstetric injury.

Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Background

The hospital admission rate is high in patients treated with peritoneal dialysis (PD), and the length of stay (LOS) in the hospital is a key indicator of medical resource allocation. This study aimed to develop a scoring tool for predicting prolonged LOS (pLOS) in PD patients by combining machine learning and traditional logistic regression (LR).

Methods

This study was based on patient data collected using the Hospital Quality Monitoring System (HQMS) in China. Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. The model with the best prediction performance was used to identify predictive factors contributing to the outcome. A multivariate LR model based on the identified predictors was then built to derive the score assigned to each predictor. Finally, a scoring tool was developed, and it was tested by stratifying PD patients into different pLOS risk groups.

Results

A total of 22,859 PD patients were included in our study, with 25.2% having pLOS. Among the three machine learning models, the RF model achieved the best prediction performance and thus was used to identify the 10 most predictive variables for building the scoring system. The multivariate LR model based on the identified predictors showed good discrimination power with an AUROC of 0.721 in the test dataset, and its coefficients were used as a basis for scoring tool development. On the basis of the developed scoring tool, PD patients were divided into three groups: low risk (≤5), median risk [5–10], and high risk (>10). The observed pLOS proportions in the low-risk, median-risk, and high-risk groups in the test dataset were 11.4%, 29.5%, and 54.7%, respectively.

Conclusions

This study developed a scoring tool to predict pLOS in PD patients. The scoring tool can effectively discriminate patients with different pLOS risks and be easily implemented in clinical practice. The pLOS scoring tool has a great potential to help physicians allocate medical resources optimally and achieve improved clinical outcomes.

Improving sensitivity of the connective tissue disease screening questionnaire: A comparative study of various scoring methods

OBJECTIVES

Early detection of autoimmune rheumatic diseases is crucial given their high morbidity and mortality and short window of opportunity to improve patient outcomes. Self-administered screening questionnaires such as the connective tissue disease screening questionnaire (CSQ) have been shown to promote early detection of autoimmune rheumatic diseases. However, optimal scoring of screening questionnaires may differ with prevalence of clinical features and changes in classification criteria. We compared the performance of 3 scoring methods for the CSQ for early detection of autoimmune rheumatic diseases in a multi-ethnic Asian population.

METHODS

Patients who were newly referred for evaluation of possible autoimmune rheumatic diseases were invited to answer the cross-culturally adapted CSQ. Detection of autoimmune rheumatic diseases using 1) the original CSQ scoring, 2) a modified CSQ scoring and 3) a scoring based on current classification criteria, were compared to classification of autoimmune rheumatic diseases by classification criteria.

RESULTS

Of 819 participants, 85 were classified as having autoimmune rheumatic diseases screened for by the adapted CSQ. The original CSQ scoring yielded relatively lower sensitivities in detecting both any and individual autoimmune rheumatic diseases (67% and 20-57%, respectively) compared to the modified CSQ scoring (81% and 60-73%, respectively) and the scoring based on current classification criteria (89% and 50-88%, respectively).

CONCLUSION

The adapted CSQ with the classification criteria-based scoring achieved relatively high sensitivities in detecting autoimmune rheumatic diseases, suggesting this could be employed as the first step in population screening.

Original and Modified Graeb Score Correlation With Intraventricular Hemorrhage and Clinical Outcome Prediction in Hyperacute Intracranial Hemorrhage

Background and Purpose- The Graeb score is a visual rating scale of intraventricular hemorrhage (IVH) on noncontrast head CT. Little data exist in the hyperacute (<6 hour) period for reliability and predictive value of the modified Graeb Score (mGS) or the original Graeb Score (oGS) for clinical outcomes or their correlation with quantitative IVH volumes. Methods- A retrospective analysis of multicenter prospective intracranial hemorrhage study was performed. oGS and mGS inter-observer agreement and IVH volume correlation on the baseline noncontrast head CT were calculated by intraclass correlation coefficient and Pearson coefficient respectively. Predictors of poor outcome (modified Rankin Scale scores ≥4) at 3 months were identified using a backward stepwise selection multivariable analysis. oGS and mGS performance for modified Rankin Scale scores ≥4 was determined by receiver operating characteristic analysis. Results- One hundred forty-one patients (65±12 years) with median (interquartile range) time to CT of 82.5 (70.3-157.5) minutes were included. IVH was observed in 43 (30%) patients. Inter-observer agreement was excellent for both oGS (intraclass correlation coefficient, 0.90 [95% CI, 0.80-0.95]) and mGS (intraclass correlation coefficient, 0.97 [95% CI, 0.84-0.99]). mGS (R=0.79; P<0.01) correlated better than oGS (R=0.71; P<0.01) with IVH volumes (P=0.02). Models of thresholded oGS and mGS were not different from a model of planimetric baseline intracranial hemorrhage and IVH volume for poor outcome prediction. Area under the curves were 0.70, 0.73, and 0.72, respectively. Conclusions- Excellent correlation for oGS and mGS with IVH volume was seen. Thresholded oGS and mGS are reasonable surrogates for planimetric IVH volume for hyperacute intracranial hemorrhage studies.

Comparison of Scoring Systems in Predicting Success of Percutaneous Nephrolithotomy

Background:

Scoring systems are useful to inform the patients about the success and complication rates of the operation prior the surgery.

Aims:

To determine the applicability of the popular scoring systems (Guy’s, stone size, tract length, obstruction, number of involved calices, and essence/stone density and Clinical Research Office of the Endourological Society) by means of examining preoperative data of patients treated with percutaneous nephrolithotomy.

Study Design:

Cross sectional study.

Methods:

We retrospectively reviewed files of the patients who had undergone percutaneous nephrolithotomy in our center between 2011 and 2015. Excluded from the study were patients aged <18 years, and those who were not assessed preoperatively with computed tomography. Preoperative computed tomography images of all patients were assessed by a single observer, and patients were graded based on three scoring system. Demographic data were analyzed along with perioperative data (operation, fluoroscopy, length of hospital stay, changes in hematocrit values, location, and number of access sites, stone-free and complication rates).

Results:

A total of 298 patients who had been treated with 300 procedures were enrolled into the study. Mean age, stone burden, number of stones, and density were 48.1±12.9 years, 663.5±442.8 mm², 1.8±1.1 and 888.3±273 HU respectively. Scores of the cases based on Guy’s, stone size, tract length, obstruction, number of involved calices, and essence/stone density, and Clinical Research Office of the Endourological Society scoring system were calculated as 2, 7.6, and 222.1 points respectively. 81.6% of the patients were stone-free. Complications were detected in 30 (9.9%) patients. Based on receiver operating characteristic curve analysis a positive correlation was detected between success rate and scoring systems, i.e., Guy’s (p=<0.001, r=-0.309), stone size, tract length, obstruction, number of involved calices, and essence/stone density (p=<0.001, r=-0.295), and Clinical Research Office of the Endourological Society (p=<0.001, r=0.426). The Clinical Research Office of the Endourological Society scoring system had the highest predictive value. The sensitivity rates rates for Guy’s, Clinical Research Office of the Endourological Society and Stone scoring system were as 78.78%, 80% and 82.34% respectively.

Conclusion:

All of scoring systems predicted correctly the success of the percutaneous nephrolithotomy procedures. The Clinical Research Office of the Endourological Society scoring system had the highest predictive value.

Comparison of neuroendocrine dysfunction in patients with adamantinomatous and papillary craniopharyngiomas

Craniopharyngiomas (CPs) are rare epithelial tumors mainly located in the sellar/parasellar region. They may be classified into two major histological subtypes, which exhibit clinical and pathological differences: Adamantinomatous CP (ACP) and papillary CP (PCP). The aim of the present study was to compare the neuroendocrine dysfunction between ACP and PCP prior to and after surgical resection. According to their pathological classification, the patients were assigned to the ACP group or the PCP group. The neuroendocrine dysfunction in these two CP subtypes was evaluated using a scoring method prior to and after surgery. A total of 741 patients with CPs were included in the present study, of which 622 were ACP and 119 were PCP. The scores on the tumor mass effect, hypothalamic dysfunction and pituitary-target gland axis dysfunction, as well as the incidence of central diabetes insipidus in the PCP group were all significantly higher than those in the ACP group, regardless of whether the surgery had been performed or not (all P<0.05). After surgery, the scores on the tumor mass effect were significantly decreased in the ACP and PCP patients (all P<0.05), while the scores on hypothalamic dysfunction and pituitary-target gland axis dysfunction, as well as the incidence of central diabetes insipidus were all significantly increased in ACP and PCP patients (all P<0.05). Prior to and after surgery, the PCP variant exhibited a greater damage to the neuroendocrine function compared with the ACP variant.

Evaluation of the "assessment for continuous treatment with hepatic arterial infusion chemotherapy" scoring system in patients with advanced hepatocellular carcinoma

AIM

Sorafenib is the recommended standard of care for advanced hepatocellular carcinoma (HCC) patients. However, hepatic arterial infusion chemotherapy (HAIC) is a treatment option in Asia. We recently developed the assessment for continuous treatment with HAIC (ACTH) score to guide decision-making for continuous HAIC treatment. The purpose of this study was to validate the utility of the ACTH score in a dedicated cohort.

METHODS

One hundred and thirty-one patients with advanced HCC were enrolled in this study (90 in the training group and 41 in the validation group). The point score (range, 0-3) was calculated as follows: Child-Pugh score before HAIC (A = 0, B = 1), α-fetoprotein (AFP) response (yes = 0, no = 1), and des-γ-carboxy prothrombin (DCP) response (yes = 0, no = 1). The AFP and DCP responses were assessed 2 weeks after HAIC induction; a positive response was defined as a reduction of ≥20% from the baseline.

RESULTS

The DCP response in the validation group was significantly associated with treatment response, and the median survival time (MST) was longer in patients with an ACTH score ≤1 (15.9 months) than in those with a score ≥2 (7.0 months; P = 0.002). Survival in all patients showed significant stratification according to the ACTH score; the MSTs associated with scores of 0, 1, 2, and 3 points were 21.7, 14.4, 9.5, and 3.8 months, respectively.

CONCLUSION

The ACTH score can aid in the therapeutic assessment and continued treatment planning of HCC patients receiving HAIC.

A prospective observational study comparing criteria-based discharge method with traditional time-based discharge method for discharging patients from post-anaesthesia care unit undergoing ambulatory or outpatient minor surgeries under general anaesthesia

Background and Aims:

Mostly, institutions in India have single post-anaesthesia care unit (PACU) which follows traditional time-based discharge (TBD) method. Recently, it has been classified into PACU Phase I and Phase II, and criteria-based discharge (CBD) method has been used. This study primarily compares CBD versus TBD methods in moving patients through PACU, and other non-clinical factors causing delay in shifting.

Methods:

One hundred patients, aged 18–65 years, American Society of Anesthesiologist's physical status I and II, scheduled for elective minor surgeries under general anaesthesia were studied. White's fast-track score in operating room (OR) and modified Aldrete's score (CBD time) in PACU were recorded. Patients were scheduled to discharge at 60 min based on TBD method. The mean CBD time and actual discharge time from PACU were statistically compared with TBD time. Other non-clinical factors delaying the discharge were also studied.

Results:

Eighty-five percent of patients achieved acceptable White's fast-track score in OR. The TBD time (60 min) was compared with the mean CBD time (10.70 ± 2.56 min) and actual discharge time (79.75 ± 12.98 min), which were found to be statistically significant. Primarily, anaesthesiologists' busy schedule was accountable for delay in discharge.

Conclusion:

The study concluded that in patients undergoing ambulatory minor surgeries, discharge times based on Criterion Based Discharge scoring systems such as modified Aldrete's and White's-fast are significantly lower in PACU Phase I as compared to the traditional Time Based Discharge method.

Inter-rater reliability of the Hayes Ability Screening Index in a sample of Australian prisoners

BACKGROUND

Reliable ascertainment of intellectual disability (ID) is important to identify those with special needs, in order for those needs to be met in the criminal justice system. Although the Hayes Ability Screening Index (HASI) is valid and widely used for the identification of possible ID, the risk of inter-rater bias between researchers when scoring the HASI has not yet been established. The current paper estimates the inter-rater reliability of the HASI in a sample of Indigenous and non-Indigenous prisoners in Western Australia.

METHODS

We estimated intra-class correlation coefficients (ICC) for the consistency of agreement among three blinded raters using a two-way random-effects model assessing the inter-rater agreement of the HASI. Kappa was also estimated for the dichotomous HASI screening threshold outcome between the raters.

RESULTS

The HASI exhibited very good within-subject consistency of agreement for Section B (ICC = 0.95; 95%CI:0.94-0.96), Section C (ICC = 0.97; 95%CI: 0.96-0.98) and Section D (ICC = 0.90; 95%CI: 0.87-0.92) subscales and for the total scaled score (ICC = 0.97; 95%CI: 0.96-0.98). The inter-rater reliability of the dichotomous adult ID screening threshold (<85) was also very good (Kappa = 0.95).

CONCLUSIONS

The current study provides new evidence that the HASI has a low risk of bias from between-rater scoring and can be reliably scored by both non-clinicians and clinicians with little training, when administered in prison settings. Pre-scoring training should focus on the more subjective 'clock-drawing' section, in order to maximise inter-rater reliability.

Validity test of the IPD-Work consortium approach for creating comparable job strain groups between Job Content Questionnaire and Demand-Control Questionnaire

OBJECTIVES

This study aims to test the validity of the IPD-Work Consortium approach for creating comparable job strain groups between the Job Content Questionnaire (JCQ) and the Demand-Control Questionnaire (DCQ).

MATERIAL AND METHODS

A random population sample (N = 682) of all middle-aged Malmö males and females was given a questionnaire with the 14-item JCQ and 11-item DCQ for the job control and job demands. The JCQ job control and job demands scores were calculated in 3 different ways: using the 14-item JCQ standard scale formulas (method 1); dropping 3 job control items and using the 11-item JCQ standard scale formulas with additional scale weights (method 2); and the approach of the IPD Group (method 3), dropping 3 job control items, but using the simple 11-item summation-based scale formulas. The high job strain was defined as a combination of high demands and low control.

RESULTS

Between the 2 questionnaires, false negatives for the high job strain were much greater than false positives (37-49% vs. 7-13%). When the method 3 was applied, the sensitivity of the JCQ for the high job strain against the DCQ was lowest (0.51 vs. 0.60-0.63 when the methods 1 and 2 were applied), although the specificity was highest (0.93 vs. 0.87-0.89 when the methods 1 and 2 were applied). The prevalence of the high job strain with the JCQ (the method 3 was applied) was considerably lower (4-7%) than with the JCQ (the methods 1 and 2 were applied) and the DCQ. The number of congruent cases for the high job strain between the 2 questionnaires was smallest when the method 3 was applied.

CONCLUSIONS

The IPD-Work Consortium approach showed 2 major weaknesses to be used for epidemiological studies on the high job strain and health outcomes as compared to the standard JCQ methods: the greater misclassification of the high job strain and lower prevalence of the high job strain.

Comparing Simple Scoring With IRT Scoring of Personality Measures: The Navy Computer Adaptive Personality Scales

This article analyzes data from U.S. Navy sailors (N = 8,956), with the central measure being the Navy Computer Adaptive Personality Scales (NCAPS). Analyses and results from this article extend and qualify those from previous research efforts by examining the properties of the NCAPS and its adaptive structure in more detail. Specifically, this article examines item exposure rates, the efficiency of item use based on item response theory (IRT)-based Expected A Posteriori (EAP) scoring, and a comparison of IRT-EAP scoring with much more parsimonious scoring methods that appear to work just as well (stem-level scoring and dichotomous scoring). The cutting-edge nature of adaptive personality testing will necessitate a series of future efforts like this: to examine the benefits of adaptive scoring schemes and novel measurement methods continually, while pushing testing technology further ahead.

Panning for the gold in health research: incorporating studies' methodological quality in meta-analysis

BACKGROUND

Systematic reviews now routinely assess methodological quality to gauge the validity of the included studies and of the synthesis as a whole. Although trends from higher quality studies should be clearer, it is uncertain how often meta-analyses incorporate methodological quality in models of study results either as predictors, or, more interestingly, in interactions with theoretical moderators.

OBJECTIVE AND METHODS

We survey 200 meta-analyses in three health promotion domains to examine when and how meta-analyses incorporate methodological quality.

RESULTS

Although methodological quality assessments commonly appear in contemporary meta-analyses (usually as scales), they are rarely incorporated in analyses, and still more rarely analysed in interaction with theoretical determinants of the success of health promotions. The few meta-analyses (2.5%) that did include such an interaction analysis showed that moderator results remained significant in higher quality studies or were present only among higher quality studies. We describe how to model quality interactively with theoretically derived moderators and discuss strengths and weaknesses of this approach and in relation to current meta-analytic practice.

CONCLUSIONS

In large literatures exhibiting heterogeneous effects, meta-analyses can incorporate methodological quality and generate conclusions that enable greater confidence not only about the substantive phenomenon but also about the role that methodological quality itself plays.