Structured Reporting in Radiology Residency: A Standardized Approach to Assessing Interpretation Skills and Competence
Bibliographic record
Abstract
The field of radiology heavily relies on image interpretation and reporting. Radiology residents undergo evaluations primarily based on their interpretation skills, often encountering varied cases with differing complexities. Assessing resident performance in such a diverse setting poses challenges due to variability in judgment among assessors. One aspect of training that can be standardized is the reporting process. Developing a structured reporting system could aid in evaluating resident milestones and achievement of Entrustable Professional Activities (EPAs), facilitating standardized assessment and comparison among peers. From our experiences, we describe a logical reasoning pathway followed by residents in their training, progressing from recognizing abnormalities to describing findings, identifying associated positive and negative findings, and recommending appropriate management. Each step provides evidence of milestone achievement and can be assessed through structured reporting. We propose that a grading system can be applied to assess perception skills, description accuracy, recognition of associated findings, formulation of differential diagnoses, recommendations, and consultation with clinicians. Comparison between junior and senior resident reports allows for monitoring progression and identifying areas for improvement. Although implementing this grading system poses challenges, it offers potential benefits in providing standardized assessment and guiding individualized learning curves for residents. Despite its limitations, once established, the system could enhance residency training in diagnostic imaging.
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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 | 0.081 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".