Measurement of perception and interpretation skills during radiology training: utility of the script concordance approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imaging specialties require both perceptual and interpretation skills. Except in very simple cases, data perception and interpretation vary among clinicians. This variability makes for difficulty in measuring these skills with traditional assessment tools. The script concordance approach is conceived to allow standardized assessment in contexts of uncertainty. In this exploratory study, the authors tested the usefulness of the approach for assessment of perceptual and interpretation skills in radiology. A perception test (PT) and an interpretation test (IT) were designed according to the approach. Both tests used plain chest X-rays. Three groups were tested: clerkship students (20), junior residents (R1-R3; 20), senior residents (R4-R5; 20). Eleven certified radiologists, all currently appointed to chest reading, provided the answers by aggregate scoring method. Statistics included descriptive, ANOVA, regression analysis, Pearson and Spearman correlation coefficients. Cronbach alpha values were 0.79 and 0.81 for the PT and IT respectively. Score progression was statistically significant in both tests. Perception scores progressed more rapidly than interpretation scores during training. Effect size was large in discriminating low versus higher level of expertise, 2.2 (PT) and 1.6 (IT). The Pearson correlation coefficient between both tests was 0.58. Cronbach alpha coefficient values indicate reasonable reliability for both tests. The linear progression of scores, each at its own pace, and the positive and moderate magnitude of the Pearson correlation coefficient are arguments suggesting measurement of two different skills. More studies are necessary to document the approach usefulness for assessment in radiology training.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it