Assessing clinical reasoning using a script concordance test with electrocardiogram in an emergency medicine clerkship rotation
Bibliographic record
Abstract
OBJECTIVES: Script concordance tests (SCTs) can be used to assess clinical reasoning, especially in situations of uncertainty, by comparing the responses of examinees with those of emergency physicians. The examinee's answers are scored based on the level of agreement with responses provided by a panel of experts. Emergency physicians are frequently uncertain in the interpretation of ECGs. Thus, the aim of this study was to validate an SCT combined with an ECG. METHODS: An SCT-ECG was developed. The test was administered to medical students, residents and emergency physicians. Scoring was based on data from a panel of 12 emergency physicians. The statistical analyses assessed the internal reliability of the SCT (Cronbach's α) and its ability to discriminate between the different groups (ANOVA followed by Tukey's post hoc test). RESULTS: The SCT-ECG was administered to 21 medical students, 19 residents and 12 emergency physicians. The internal reliability was satisfactory (Cronbach's α=0.80). Statistically significant differences were found between the groups (F(0.271)=21.07; p<0.0001). Moreover, significant differences (post hoc test) were detected between students and residents (p<0.001), students and experts (p<0.001), and residents and experts (p=0.017). CONCLUSIONS: This SCT-ECG is a valid tool to assess clinical reasoning in a context of uncertainty due to its high internal reliability and its ability to discriminate between different levels of expertise.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".