The Script Concordance Test: A New Tool Assessing Clinical Judgement in Neurology
Bibliographic record
Abstract
BACKGROUND: Clinical judgment, the ability to make appropriate decisions in uncertain situations, is central to neurological practice, but objective measures of clinical judgment in neurology trainees are lacking. The Script Concordance Test (SCT), based on script theory from cognitive psychology, uses authentic clinical scenarios to compare a trainee's judgment skills with those of experts. The SCT has been validated in several medical disciplines, but has not been investigated in neurology. METHODS: We developed an Internet-based neurology SCT (NSCT) comprising 24 clinical scenarios with three to four questions each. The scenarios were designed to reflect the uncertainty of real-life clinical encounters in adult neurology. The questions explored aspects of the scenario in which several responses might be acceptable; trainees were asked to judge which response they considered to be best. Forty-one PGY1-PGY5 neurology residents and eight medical students from three North American neurology programs (McGill, Calgary, and Mayo Clinic) completed the NSCT. The responses of trainees to each question were compared with the aggregate responses of an expert panel of 16 attending neurologists. RESULTS: The NSCT demonstrated good reliability (Cronbach alpha = 0.79). Neurology residents scored higher than medical students and lower than attending neurologists, supporting the test's construct validity. Furthermore, NSCT scores discriminated between senior (PGY3-5) and junior residents (PGY1-2). CONCLUSIONS: Our NSCT is a practical and reliable instrument, and our findings support its construct validity for assessing judgment in neurology trainees. The NSCT has potentially widespread applications as an evaluation tool, both in neurology training and for licensing examinations.
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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 | 0.010 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".