Peer review quality assurance in stereotactic body radiotherapy planning: the impact of case volume
Bibliographic record
Abstract
Abstract Purpose: Peer review is an essential quality assurance component of radiation therapy planning. A growing body of literature has demonstrated substantial rates of suggested plan changes resulting from peer review. There remains a paucity of data on the impact of peer review rounds for stereotactic body radiation therapy (SBRT). We therefore aim to evaluate the outcomes of peer review in this specific patient cohort. Methods and materials: We conducted a retrospective review of all SBRT cases that underwent peer review from July 2015 to June 2018 at a single institution. Weekly peer review rounds are grouped according to cancer subsite and attended by radiation oncologists, medical physicists and medical radiation technologists. We prospectively compiled ‘learning moments’, defined as cases with suggested changes or where an educational discussion occurred beyond routine management, and critical errors, defined as errors which could alter clinical outcomes, recorded prospectively during peer review. Plan changes implemented after peer review were documented. Results: Nine hundred thirty-four SBRT cases were included. The most common treatment sites were lung (518, 55%), liver (196, 21%) and spine (119, 13%). Learning moments were identified in 161 cases (17%) and translated into plan changes in 28 cases (3%). Two critical errors (0.2%) were identified: an inadequate planning target volume margin and an incorrect image set used for contouring. There was a statistically significantly higher rate of learning moments for lower-volume SBRT sites (defined as ≤30 cases/year) versus higher-volume SBRT sites (29% vs 16%, respectively; p = 0.001). Conclusions: Peer review for SBRT cases revealed a low rate of critical errors, but did result in implemented plan changes in 3% of cases, and either educational discussion or suggestions of plan changes in 17% of cases. All SBRT sites appear to benefit from peer review, though lower-volume sites may require particular attention.
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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 | 0.062 | 0.481 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".