Is a web-based concordance test feasible to assess therapeutic decision-making skills in a French context?
Bibliographic record
Abstract
BACKGROUND: At the end of undergraduate medical curriculum, a written simulation-based examination is used in France to assess therapeutic decision-making skills and to rank students for the purpose of matching their training specialties. However, this examination based on a single assessment method remains a subject of debate. AIM: To study the feasibility of a web-based Concordance test for therapeutic decision-making assessment. METHODS: A 12 clinical-case Concordance test was developed based on objectives for the undergraduate training program. The test was administered on line to candidates with different levels of clinical experience. Fifteen therapeutic teachers constituted the reference panel. Data analysis included analysis of variance, post-hoc test, and Cronbach's alpha. RESULTS: One hundred and seventy participants (113 students, 34 residents, 23 physicians) fully completed the free-access test on line with no technical problems. Differences between the mean scores for groups were significant (p < 0.001). Significant differences occurred between fourth year students and residents (p < 0.001), fourth year students and physicians (p = 0.001). No difference was found between residents and physicians. Reliability coefficient was 0.67. CONCLUSION: A web-based Concordance test in the field of therapeutic decision-making was considered feasible in a French learning environment. Further research is warranted to determine its usefulness as a part of the National Examination.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".