Performance results for a workstation-integrated radiology peer review quality assurance program: Table 1
Bibliographic record
Abstract
OBJECTIVE: To assess review completion rates, RADPEER score distribution, and sources of disagreement when using a workstation-integrated radiology peer review program, and to evaluate radiologist perceptions of the program. DESIGN: Retrospective review of prospectively collected data. SETTING: Large private outpatient radiology practice. PARTICIPANTS: Radiologists (n = 66) with a mean of 16.0 (standard deviation, 9.2) years of experience. INTERVENTIONS: Prior studies and reports of cases being actively reported were randomly selected for peer review using the RADPEER scoring system (a 4-point scale, with a score of 1 indicating agreement and scores of 2-4 indicating increasing levels of disagreement). MAIN OUTCOME MEASURES: Assigned peer review completion rates, review scores, sources of disagreement and radiologist survey responses. RESULTS: Of 31 293 assigned cases, 29 044 (92.8%; 95% CI 92.5-93.1%) were reviewed. Discrepant scores (score = 2, 3 or 4) were given in 0.69% (95% CI 0.60-0.79%) of cases and clinically significant discrepancy (score = 3 or 4) was assigned in 0.42% (95% CI 0.35-0.50%). The most common cause of disagreement was missed diagnosis (75.2%; 95% CI 66.8-82.1%). By anonymous survey, 94% of radiologists felt that peer review was worthwhile, 90% reported that the scores they received were appropriate and 78% felt that the received feedback was valuable. CONCLUSION: Workstation-based peer review can increase completion rates and levels of radiologist acceptance while producing RADPEER scores similar to those previously reported. This approach may be one way to increase radiologist engagement in peer review quality assurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".