The Canadian partnership for quality radiotherapy: A model for radiation treatment quality and safety.
Bibliographic record
Abstract
266 Background: Innovative, overarching national, and international strategies for the assurance of safe and high quality radiation treatment (RT) are needed, given the rate of technologic innovation and shifts in traditional roles associated with RT planning and delivery. Methods: The Canadian Partnership for Quality Radiotherapy (CPQR) was created as an interprofessional alliance of the Canadian Association of Radiation Oncology (CARO), Canadian Organization of Medical Physicists (COMP), Canadian Association of Medical Radiation Technologists (CAMRT) and Canadian Partnership Against Cancer (CPAC), aimed at developing and promoting coordinated national strategies for high quality and safe RT. Results: A steering committee of national leaders from each profession and content experts in RT quality and safety was created, with financial and strategic backing provided by the federal government through CPAC. The vision and strategy were communicated broadly to the RT treatment community and to other stakeholders provincially, nationally, and internationally. ‘Quality Assurance Guidance for Canadian Radiation Treatment Programs’ was published online in April 2011 and empowered programs across the country to evaluate their internal procedures against these indicators. This document was downloaded over 875 times in the first 6 months, a measure of the demand and broad uptake in Canada and internationally. This early success has fostered other foci of activity, including engagement by the Canadian medical physics community around synthesis of detailed quality control guidelines for equipment, a dialogue about national incident reporting, and support from international partners in relation to collaborative programs. The goal is to incorporate RT quality and safety indicators into national accreditation programs for cancer care to assure long-term sustainability. Conclusions: CPQR has established a model for quality and safety that is fostering the evolution of a new national quality culture. This model is applicable to other jurisdictions, recognizing that a coordinated international approach to setting guidelines and standards will ultimately lead to higher quality and safer RT on a global scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 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".