Script concordance tests: A call for action in dental education
Bibliographic record
Abstract
The Script Concordance Test (SCT) is an educational tool that aims to assess the ability to interpret medical information under conditions of uncertainty. It is widely used and validated in health education, but almost unknown in dentistry. Based on authentic clinical problem-solving situations, it allows to assess clinical reasoning that experienced health workers develop over the years. A specific scoring system, dedicated to SCT, considers the variability of responses of practitioners in the same clinical situations. Finally, the scores generated by SCT reflect the respondents' ability to interpret clinical data compared to experienced clinicians. This article aims to familiarise the dental educators' community with SCT construction, optimisation and its possible applications.
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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 | 0.328 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".