Assessment of Undergraduate Clinical Reasoning in Geriatric Medicine: Application of a Script Concordance Test
Bibliographic record
Abstract
A challenging aspect of geriatric practice is that it often requires decision-making under conditions of uncertainty. The Script Concordance Test (SCT) is an assessment tool designed to measure clinical data interpretation, an important element of clinical reasoning under uncertainty. The purpose of this study was to develop and analyze the validity of results of an SCT administered to undergraduate students in geriatric medicine. An SCT consisting of 13 cases and 104 items covering a spectrum of common geriatric problems was designed and administered to 41 undergraduate medical students at a medical school in São Paulo, Brazil. A reference panel of 21 practicing geriatricians contributed to the test's score key. The responses were analyzed, and the psychometric properties of the tool were investigated. The test's internal consistency and discriminative capacity to distinguish students from experienced geriatricians supported construct validity. The Cronbach alpha for the test was 0.84, and mean scores for the experts were found to be significantly higher than those of the students (80.0 and 70.7, respectively; P < .001). This study demonstrated robust evidence of reliability and validity of an SCT developed for use in geriatric medicine for assessing clinical reasoning skills under conditions of uncertainty in undergraduate medical students. These findings will be of interest to those involved in assessing clinical competence in geriatrics and will have important potential application in medical school examinations.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".