Improving the quality of care for patients receiving radiation therapy: Increasing the proportion of radiation treatment plans undergoing peer review in Ontario.
Bibliographic record
Abstract
136 Background: Peer review of radiation treatment (RT) plans is recognized as an essential component of quality assurance programs in radiation medicine (Marks et al., 2013). The benefits of peer review include: (1) identifying errors that may compromise treatment outcomes, (2) enhancing safety by promoting standardization, and (3) promoting greater attention to detail in RT staff. Current state analysis conducted in 2011 identified considerable variation in the proportion of cases undergoing peer review across Ontario’s 14 cancer centres (Brundage et al., 2013). In 2012, Cancer Care Ontario launched an initiative to ensure all patients receiving radical/adjuvant radiotherapy have the benefit of peer review of their RT plans. Methods: A multi-professional project team was established to conduct site visits to promote peer review at the cancer centres. They also provided guidance on the organization of peer review rounds so that the activity could be incorporated into local workflows. The education, training, methods, and a centralized reporting infrastructure were developed in collaboration with centres over a one year ramp-up phase and patient-level data was available to the centres for audit purposes. The reporting infrastructure enabled reporting of (1) the proportion of cases peer reviewed and (2) the timing of peer review – prior to treatment, <25% dose delivered, >25% dose delivered. Results: Data for each centre is now a key quality metric and is publicly reported (see Cancer System Quality Index at http://www.csqi.on.ca/). The target for year-one of the project (2013-14) – the proportion of cases to be peer reviewed – was set at 50% with the intent that 100% of cases will be peer reviewed within the next two years. In the ramp-up year, the proportion of cases peer reviewed increased across all centres, though high variation still exists between centres. Conclusions: This initiative demonstrates that it is possible to substantially increase peer review activities on a jurisdictional basis. Key success factors include: a dedicated project team, buy-in and confidence in data quality from centres, investment in education and training, and commitment to public reporting.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".