A systematic review and meta-analysis on the impact of institutional peer review in radiation oncology
Bibliographic record
Abstract
BACKGROUND: Radiotherapy peer review is recognized as a key component of institutional quality assurance, though the impact is ill-defined. We conducted the first systematic review and meta-analysis to date to quantify the impact of institutional peer review on the treatment planning workflow including radiotherapy contours, prescription and dosimetry. METHODS: We searched several medical and healthcare databases from January 1, 2000, to May 25, 2024, for papers that report on the impact of institutional radiotherapy peer review on treatment plans. We conducted random-effects meta-analyses of proportions to summarize the rates of any change recommendation and major change recommendation (suggesting re-planning or re-simulation due to safety concerns) following peer review processes. To explore differences in change recommendations dependent on location, radiotherapy intent, technique, and peer review structure characteristics, we conducted analyses of variance. RESULTS: Of 9,487 citations, we identified 55 studies that report on 96,444 case audits in 10 countries across various disease sites. The pooled proportion of any change recommendation was 28 % (95 %CI = 21-35) and major change recommendation was 12 % (95 %CI = 7-18). Proportions of change recommendation were not impacted by any treatment characteristics. The most common reasons for change recommendation include target volume delineation (25/55; 45 %), target dose prescription (18/55; 33 %), organ at risk dose prescription (5/55; 9 %), and organ at risk volume delineation (3/55; 5 %). CONCLUSIONS: Our review provides evidence that peer review results in treatment plan change recommendations in over one in four patients. The results suggest that some form of real-time, early peer review may be beneficial for all cases, irrespective of treatment intent or RT technique.
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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 | 0.165 | 0.444 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.051 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".