A comprehensive checklist for reporting the use of OSCEs
Bibliographic record
Abstract
BACKGROUND: The Objective Structured Clinical Examination (OSCE) has experienced an explosion of use which has rarely been accompanied by systematic investigations on its validity, reliability and feasibility. A systematic review of OSCE was undertaken as part of Best Evidence Medical Education at the Centre for Evidence Based Medicine of the Faculty of Medicine of the University of Lisbon. Several problems were identified with published papers relating to completeness of information presented, methodological issues or the use of terminology. AIM: To identify a need for standardization within the reporting of OSCE studies in medical education based in the first 104 papers of the aforementioned review. METHOD: Two independent reviewers coded each paper. RESULTS: The most important problem identified was the lack of information, followed by the degree of inconsistency when reporting on OSCEs (papers with missing data and papers where data was given in a way that interpretation is difficult or impossible in terms of evidence; heterogeneity in reporting, lack of a standardized vocabulary, statistical errors and lack of structure within reporting). CONCLUSIONS: The authors present a 'Comprehensive Checklist for those describing the use of OSCEs in the report of educational literature' as an attempt to encourage better report standards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.231 | 0.413 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.038 | 0.022 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".