How specific is case specificity?
Bibliographic record
Abstract
OBJECTIVES: Case specificity implies that success on any case is specific to that case. In examining the sources of error variance in performance on case-based examinations, how much error variance results from differences between cases compared with differences between items within cases? What is the optimal number of cases and questions within cases to maximise test reliability given some fixed period of examination time? METHODS: G and D generalisability studies were conducted to identify variance components and reliability for each examination analysed, and to optimise the reliability of the given test composition (1, 1.5, 2, 3, 4 and 5 questions per case), using data from 3 key features examinations of the Medical Council of Canada (n = 6342 graduating medical students), each of which consisted of about 35 written cases followed by 1- questions regarding specific key elements of data gathering, diagnosis and/or management. RESULTS: The smallest variance component was due to subjects; the variance due to subject-item interaction was over 5 times the interaction with cases (on average, 0.1106 compared with 0.0195). Relatively little variance was due to differences between cases; about 80% of the error variance was due to variability in performance among items within cases. The D study showed that reliability varied between 0.541 and 0.579, was least with 1 item per case and highest at 2 and 3 items per case. CONCLUSIONS: The main source of error variance was items within cases, not cases, and the optimal strategy in terms of enhancing reliability would use cases with 2-3 items per case.
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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 | 0.051 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".