Measurement of perception and interpretation skills during radiology training: utility of the script concordance approach
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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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 | 0.020 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".