Assessing the validity of an OSCE developed to assess rare, emergent or complex clinical conditions in endocrinology & metabolism
Bibliographic record
Abstract
BACKGROUND: Assessment of emergent, rare or complex medical conditions in Endocrinology and Metabolism (E&M) is an integral component of training. However, data is lacking on how this could be best achieved. The purpose of this study was to develop and administer an Objective Structured Clinical Examination (OSCE) for E&M residents, and to gather validity evidence for its use. METHODS: A needs assessment survey was distributed to all Canadian E&M Program Directors and recent graduates to determine which topics to include in the OSCE. The top 5 topics were selected using a modified Delphi technique. OSCE cases based on these topics were subsequently developed. Five E&M residents (PGY4-5) and five junior Internal Medicine (IM) residents participated in the OSCE. Performance of E&M and IM residents was compared and results were analyzed using a Generalizability study. Examiners and candidates completed a survey following the OSCE to evaluate their experiences. RESULTS: = 0.75). Overall reliability of the OSCE was 0.74. Standard setting using a borderline regression method resulted in a pass rate of 100 % of E&M residents and 0 % of IM residents. All residents felt the OSCE had high value for learning as a formative exam. CONCLUSIONS: The E&M OSCE is a feasible method for assessing emergent, rare and complex medical conditions and this study provides validity evidence to support its use in a competency-based curriculum.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".