Peer review in radiotherapy (RT): A national quality improvement initiative.
Bibliographic record
Abstract
221 Background: Peer review (PR) in Radiation Oncology has been shown to be effective in improving quality of treatment by detecting and correcting deficiencies in proposed treatment plans prior to treatment. PR is also effective in: guiding departmental treatment planning policies and processes; reducing variation in practice; providing a venue for multi-disciplinary communication, and increasing staff and trainee awareness of evolving treatment processes. The importance of PR is reflected in the inclusion of 3 PR-specific quality indicators in the Canadian Partnership for Quality Radiotherapy QA Guidelines for Radiation Oncology programs. Given this endorsement, we aim to enhance PR implementation across all Canadian cancer centres using a knowledge-translation and implementation framework. Methods: This project will facilitate increased uptake of PR in Canadian RT programs by implementing the top-down model used with success in Ontario. This model has several key components, including: a) engaging the leadership of provincial cancer agencies to promote PR at every Provincial cancer centre; b) providing modest financial support for the acquisition of the required hardware and/or staff time for coordinating PR activities; c) systematic collection of each centre’s baseline PR activities, perceived barriers and potential facilitators of PR at each centre; d) creation of a continuous quality improvement cycle by monitoring PR quality indicators over time; e) systematic knowledge and information sharing regarding effective PR processes. Results: Funding for this initiative was obtained from the Canadian Partnership against Cancer (CPAC) in April 2014. A steering committee consisting of stakeholders from across Canada has been struck and provincial launches, based on the tenets used in Ontario, have commenced in 7 of 13 provinces with others expressing interest. A national survey to obtain baseline data relating to PR activities, perceived barriers, and facilitators is underway and will be reported. Conclusions: Preliminary evidence suggests a “snowball effect” of increasing PR uptake across Canada. The implementation model could be applied in other jurisdictions interested in increasing PR in radiation oncology.
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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 | 0.169 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".