Assessment of clinical reasoning in the context of uncertainty: the effect of variability within the reference panel
Bibliographic record
Abstract
The Script Concordance Test (SCT) assesses reasoning in the context of uncertainty. Because there is no single correct answer, scoring is based on the comparison of answers provided by examinees with those provided by members of a reference panel made up of experienced practitioners. The study aimed to assess the discriminatory power of the SCT based on the variability of the reference panel's answers. Items from a bank covering different family medicine domains were classified into 3 groups according to the degree of variability of answers provided by a pool of experienced doctors. A variability index (mean squared error) was used to select items in the low, moderate and high variability categories. A 102-item test (Cronbach's alpha 0.70), made up of 3 subtests of each category, was administered to 3 contrasting groups in family medicine: 157 clerkship students, 30 residents and 30 practising doctors. anova and effect size (ES) were used to quantify and test the discrimination power of the 3 subtests. The high variability subtest showed high effect size for discrimination between extreme groups (ES = 1.5; F = 16.3, P < 0.001), whereas the moderate variability subtest showed less effect size (ES = 0.56; F = 57, P = 0.041). The low variability subtest did not discriminate significantly (ES = 0.31; F = 2.9, P = 0.06). Variability of answers within the reference panel is a key component of the discriminatory power of the SCT. In accordance with theory, the presence of variability ensures discrimination between levels of clinical experience. These results imply important considerations for the construction of efficient SCTs.
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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 | 0.039 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".