A web-based test of residents' skills in diagnostic radiology.
Bibliographic record
Abstract
PURPOSE: To develop an objective, Web-based tool for evaluating residents' knowledge of diagnostic radiology. METHODS: We developed and tested a Web-based evaluation tool (the Diagnostic Radiology Skills Test) that consists of 3 tests, one in each of 3 domains of diagnostic radiology: chest, gastrointestinal, and musculoskeletal imaging. Each test comprises 30 cases representing a range of difficulty in the domain, including normal states, normal variants, typical cases of common diagnoses, and cases with more subtle findings. Cases are presented with a long menu of domain-specific possible diagnoses (response options), each coded for diagnostic appropriateness. Our subjects were 21 residents in postgraduate year (PGY) 2 to 5 and 11 experts in diagnostic radiology. Subjects accessed the tool via a Web site on our Web server. Residents' test results were compared for reliability and validity across domain, case, and training level. In addition, results were correlated with commonly used established and objective evaluation tools. RESULTS: The tool demonstrated consistent monotonic improvement in performance with training level. It showed acceptable reliability in discriminating between residents at different performance levels, both within and across training levels (r= 0.53 within level and 0.69 across levels). Test results also had concurrent validity against the American College of Radiology In-Training Examination, a widely accepted objective assessment tool (r = 0.65, P < 0.01), and 2 Objective Structured Clinical Examinations (OSCEs) focusing on diagnostic skills (r= 0.78 and r= 0.69, P < 0.01, respectively). CONCLUSIONS: Our study demonstrates the feasibility of a Web-based, standardized, objective assessment method for evaluating residents' performance.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".