Development of Best Practices of Peer Review for Lung Radiation Therapy
Bibliographic record
Abstract
PURPOSE: Peer review (PR) is an important component in ensuring high-quality lung radiotherapy (RT) plans. However, there are inconsistencies in the extent, timing, and minimum requirements for PR. We sought to develop guidelines of best practices for PR in curative lung RT through an expert consensus process. METHODS: A modified Delphi process was conducted that consisted of an initial review by a dedicated steering committee followed by a pan-Canadian, multidisciplinary Delphi panel with 3 rounds (premeeting survey, face-to-face meeting, and postmeeting ratification survey). Candidate PR elements were ranked by importance and stratified by treatment of locally advanced (LA) disease with conventional RT or stereotactic ablative body RT (SABR) for early-stage disease. RESULTS: For the LA case, 6 elements (indications for RT, gross tumor volume [GTV], clinical target volume [CTV], internal target volume [ITV], dose/fractionation, and normal lung dosimetry) were considered as essential PR elements. Of these, 90%-100% of the panel endorsed them to be important to PR, and 80% believed that the PR should be done by a second radiation oncologist (RO). In the SABR case, 6 PR elements (indications for RT, GTV, CTV/ITV, organs at risk contours, dose/fractionation, and composite plan review) were deemed essential. Of these, 90%-100% of panel members believed these elements to be important to PR and unanimously agreed that PR should be done by a second RO. CONCLUSION: A suite of PR elements for lung RT has been developed and endorsed with high consensus. This suite should serve as a basis to help to harmonize PR practices across centers and to help to develop novel PR approaches going forward.
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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 | 0.622 | 0.707 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.012 | 0.027 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".