A think-aloud study to inform the design of radiograph interpretation practice
Bibliographic record
Abstract
Models for diagnostic reasoning in radiology have been based on the observed behaviors of experienced radiologists but have not directly focused on the thought processes of novices as they improve their accuracy of image interpretation. By collecting think-aloud verbal reports, the current study was designed to investigate differences in specific thought processes between medical students (novices) as they learn and radiologists (experts), so that we can better design future instructional environments. Seven medical students and four physicians with radiology training were asked to interpret and diagnose pediatric elbow radiographs where fracture is suspected. After reporting their diagnosis of a case, they were given immediate feedback. Participants were asked to verbalize their thoughts while completing the diagnosis and while they reflected on the provided feedback. The protocol analysis of their verbalizations showed that participants used some combination of four processes to interpret the case: gestalt interpretation, purposeful search, rule application, and reasoning from a prior case. All types of processes except reasoning from a prior case were applied significantly more frequently by experts. Further, gestalt interpretation was used with higher frequency in abnormal cases while purposeful search was used more often for normal cases. Our assessment of processes could help guide the design of instructional environments with well-curated image banks and analytics to facilitate the novice's journey to expertise in image interpretation.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".