The Script Concordance Test as a Measure of Clinical Reasoning Skills in Geriatric Urinary Incontinence
Bibliographic record
Abstract
OBJECTIVES: To validate the use of a script concordance test (SCT), a tool to assess clinical reasoning in contexts of uncertainty, which are common in clinical geriatrics practice, on geriatric urinary incontinence (UI) to discriminate levels of expertise in this content area. DESIGN: A reference panel (15 geriatricians) and 12 respondents (10 senior geriatrics fellows and 2 interns) completed an online 100-item SCT test covering major topics in UI. The test was then optimized by discarding items with negative item-total correlation; the remaining 70 questions covered all major topics in UI. The test was then administered to a second group of participants with different levels of experience, mostly from the University of Miami: eight geriatricians, nine junior geriatrics fellows, 53 internal medicine residents, and 26 medical students. Investigators assessed test reliability and construct validity (to discriminate between levels of expertise). SETTING: Tertiary academic medical center and affiliated medical school. PARTICIPANTS: Medical students, internal medicine residents, geriatric medicine fellows, and practicing geriatricians. MEASUREMENTS: Seventy-item SCT. RESULTS: The Cronbach alpha for the 70-item test was 0.72. Mean scores were 75.3 ± 7.9 for geriatricians (n = 23), 69.0 ± 9.3 for senior geriatrics fellows (n = 10), 66.4 ± 6.8 for junior geriatrics fellows n = (9), 66.1 ± 5.7 for residents (n = 53), and 65.6 ± 8.5 for students (n = 26). Using analysis of variance, significant differences were found between the mean scores of the geriatricians and all other participants except senior fellows. CONCLUSION: The geriatric UI SCT demonstrated moderate reliability and evidence of construct validity, discriminating between experienced and nonexperienced physicians.
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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 | 0.007 | 0.034 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".