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 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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".