Diagnostic Radiology Residency Assessment Tools: A Scoping Review
Bibliographic record
Abstract
PURPOSE: The multifaceted nature of learning in diagnostic radiology residency requires a variety of assessment methods. However, the scope and quality of assessment tools has not been formally examined. A scoping review was performed to identify assessment tools available for radiology resident training and to evaluate the validity of these tools. METHODS: A literature search was conducted through multiple databases and on-line resources. Inclusion criteria were defined as any tool used in assessment of radiology resident competence. Data regarding residents, evaluators and specifics of each tool was extracted. Each tool was subjected through a validation process with a customized rating scale using the 5 categories of validity: content, response process, internal structure, relations to other variables, and consequences. RESULTS: evaluations were most commonly set in the United States (57%). In terms of validation, we found low adherence to modern integrated validity, with 34% of studies including a definition of validity. When specifically examining the 5 domains of validation evidence presented, most were either absent or of low rigor (70%). Only one study presented a modern definition of validation (3%, 1/35). CONCLUSION: We identified 35 evaluation tools covering a variety of competency areas. However, few of these tools have been validated. Development of new validated assessment tools or validation of existing tools is essential for the ongoing transition to a competency-based curriculum.
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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 | 0.025 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.032 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".