Evidence-based guidance for peer review “best practices” in radiation oncology.
Bibliographic record
Abstract
180 Background: Peer review (PR) is an essential component of quality assurance in radiation oncology practice, endorsed by Canadian cancer agencies as a national standard of quality care. However, reported patterns of care studies indicate that PR practice varies considerably, including which contours (e.g., target volumes, organs at risk (OARs)), which dose parameters (e.g., OAR constraints, homogeneity criteria), which decision making points (e.g., decision to treat, selection of anatomical regions), and which treatment imaging parameters are included in the PR process. Both qualitative and quantitative evidence support the need for guidance on PR best practices. Here we report processes for the establishment of best-practice guidelines/minimal standards for PR. Methods: A comprehensive literature review was done to quantify PR findings for each of 3 cancer streams: head/neck, lung, and breast. This evidence summarized which aspects of radiotherapy plans were most frequently "flagged" at PR. A modified Delphi process was used to develop cancer site-specific PR guidance documents: appropriate stakeholders were first identified; a pre-meeting survey determined opinions on which plan elements are essential/important to review; a face-to-face meeting considered the evidence base and survey results to reach consensus. Parallel processes were conducted separately for each stream. Results: The literature findings were compiled to summarize the reported proportion of plans flagged by PR, and the nature of the flag (i.e. volumes, dose, OARs, other) for each cancer stream. Stakeholders included radiation oncologists, physicists, radiation therapists, and patients, specific to each stream. Draft guidance documents were created on the basis of stakeholder consensus regarding which components of PR were deemed essential; other important or “second tier” elements were also identified. Conclusions: Guidance documents including minimum standards for PR can be successfully created based on structured consensus development informed by available evidence. This guidance promises to reduce variation in PR thereby optimizing the efficiencies of PR processes, and increasing the quality of radiation treatment plans.
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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 | 0.501 | 0.774 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.019 | 0.019 |
| Research integrity | 0.030 | 0.020 |
| Insufficient payload (model declined to judge) | 0.051 | 0.060 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".