The Oral Health Statistics Guidelines for Reporting Observational Studies and Clinical Trials in Oral Health Research: Explanation and Elaboration

Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the Oral Health Statistics (OHStat) Guidelines. Drafts were circulated to the editors of 85 oral health journals and to task force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the task force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in Journal of Dental Research Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.

The OHStat Guidelines for Reporting Observational Studies and Clinical Trials in Oral Health Research: explanation and elaboration

Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the OHstat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the Task Force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in JDR Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.

The OHStat Guidelines for Reporting Observational Studies and Clinical Trials in Oral Health Research: Explanation and Elaboration

Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research-statisticians and trialists from academia and industry-empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the OHstat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the Task Force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in JDR Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.

Structuralizing biomedical abstracts with discriminative linguistic features

OBJECTIVE

Nearly 75% of the abstracts in MEDLINE papers present in an unstructured format. This study aims to automate the reformatting of unstructured abstracts into the Introduction, Methods, Results, and Discussion (IMRAD) format. The quality of this reformatting relies on the features used in sentence classification. Therefore, we explored the most effective linguistic features in MEDLINE papers.

METHODS

We constructed a feature set consisting of bag of words, linguistic features, grammatical features, and structural features. In order to evaluate the effectiveness, which is the capability of the sentence classification with the features, three datasets from PubMed Central Open Access Subset were selected and constructed: (1) structured abstract (SA) for training, (2) unstructured RCT abstract (UA-1) and (3) unstructured general abstract (UA-2). F-score and accuracy were used to measure the effectiveness on IMRAD section level and the overall classification.

RESULTS

Adding linguistic features improves the classification of the abstract sentence from 1.2% to 35.8% in terms of accuracy in three abstract datasets. The highest accuracies achieved were 91.7% in SA, 86.3% in UA-1, and 77.9% in UA-2. Linguistic features (dimensions=15) had fewer dimensions than bag-of-words (dimensions= 1541). All representative linguistic features (n-gram and verb phrase, and noun phrase) for each section are identified in our system (available at http://abstract.bike.re.kr).

CONCLUSION

Linguistic features can be used to effectively classify sentence with low computation burden in MEDLINE abstract.

QuikScan formatting as a means to improve text recall

Purpose

QuikScan is an innovative text format that employs three prominent signaling devices – summaries, headings, and access cues – to make the reading of medium‐to‐long texts more productive. The experiments reported in this paper aim to examine the claim that QuikScan contributes to text recall.

Design/methodology/approach

In two consecutive experiments a QuikScanned text (experimental condition) was compared to a non‐QuickScanned text (control condition). In Experiment one, 41 university students read the text and then answered ten open recall questions. In Experiment two, 58 university students read the text and then wrote a summary and answered four recall questions.

Findings

In Experiment one, a statistically significant overall effect on text recall favoring QuikScan was found. Detailed analyses revealed that QuikScan mainly affected the readers' responses to higher‐order questions (d = 1.24). Experiment two showed that QuikScan led to significantly higher recall scores for the summaries. Just as in the first experiment, a strong effect on the higher‐order questions was found (d = 1.27).

Research limitations/implications

Further studies of QuikScan should include studies in naturalistic settings and should address selective reading and information navigation as well as text recall. SARA, a recent comprehensive theory of signaling, makes it possible to identify the individual functions of QuikScan's signaling devices and conduct revealing studies of QuikScan.

Practical implications

QuikScan and other innovations that improve the reading experience can potentially increase the willingness of readers to read longer documents.

Originality/value

QuikScan provides a unique combination of signaling devices. It can facilitate access and enhance text comprehension.