Composition of the panel of reference for concordance tests: Do teaching functions have an impact on examinees’ ranks and absolute scores?
Bibliographic record
Abstract
BACKGROUND: Concordance tests are designed to assess the component of uncertainty of clinical reasoning. Scoring is based on a comparison of examinees' answers with those of a panel of reference, including their variability. This allows construction of tests that are close to real clinical life, with all its complexity and ambiguity. AIM: This study was carried out to determine the effect of teaching functions of members composing the reference panels on students' scores and ranking. METHODS: A group of 80 residents in family medicine from a French University (Bobigny) completed a 72-item concordance test. The answers of two panels, each made up of 29 family physicians (teaching function versus non-teaching function), were used to generate the correction keys. RESULTS: Correlation between the sets of data obtained with the two panels is high (ICC = 0.98). Concordance scores obtained from the teaching-function panel are higher than scores obtained from the non-teaching-function panel (72.0 versus 76.3; p < 0.001). Ranking provided by the two panels was very similar. CONCLUSIONS: This legitimizes the use of non-teaching physicians on panels. Panel composition influenced absolute score values: Residents showed more concordance with their academic trainers than with community-based physicians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".