Peer-review of radiation treatment planning in a provincial radiation oncology program: A survey of current practice.
Bibliographic record
Abstract
211 Background: The use of peer-review activities in oncology is not well described as a quality improvement process. We sought to describe current patterns of practice of radiation oncology peer-review across a large Provincial Cancer program and to identifiy barriers to its use. Methods: Ontario cancer centres were surveyed. Survey item responses were typically scored using a 10-point Likert scale. The survey was administered electronically with follow-up reminders as required. The use of free-text for comments elaborating on responses was encouraged. Results: Fourteen (100%) centres responded. All rated the importance of peer-review as at least 8/10 (10=extremely important). Detection of medical error and improvement of planning processes were the highest-rated benefits of peer-review (each median 9/10). Four centres (29%) conducted peer-review in more than 80% of cases treated with curative intent; six (43%) peer-reviewed at least 50% of curative cases. Five centres (36%) reported “always” or “almost always” conducting peer-review prior to the initiation of treatment. Variation was seen in which aspects of a case were typically reviewed (e.g., GTV “almost always” reviewed in 67%; contouring of organs at risk in 50%). Five centres (46% of those with regular peer-review) reported that 5% to 9% of peer-reviewed cases were flagged as requiring a change, whereas 3 centres (27%) reported that < 2% of peer-reviewed cases required a change to be made. Five centres (36%) recorded the outcomes of peer-review on the medical record. Thirteen centres (93%) planned to expand peer-review activities; the two factors rated as most limiting to expanding peer-review were a critical mass of radiation oncologists (median score 6/10), and prioritization of peer review by the program overall (median 5/10). Conclusions: Peer review in radiation oncology practices it is now widely used as a quality assurance activity in Ontario, identifies changes to improve quality in the individual case, and improves departmental process. The development of guidelines and standards for peer-review activities, coupled with effective knowledge translation activities are recommended.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".