Collaborating with Ontario cancer centers to ensure coordinated access to safe, evidence-based, high-quality IMRT treatment.
Bibliographic record
Abstract
187 Background: In 2007, CCO identified variability in access to Intensity Modulated Radiation Therapy (IMRT) across the province. To ensure access to this technology, a three-year IMRT implementation Project was started in 2008, focusing on the development of: disease site guidelines and targets, cost-effectiveness analyses, a provincial quality assurance program, disease and discipline specific communities of practice and knowledge-sharing platforms. Methods: Needs assessments were conducted to identify educational and training needs in all disciplines. Systematic reviews were undertaken to establish disease site specific IMRT indications, set targets and conduct cost effective analyses. Educational courses and hands on expert-based coaching, were made available and a provincial IMRT quality assurance program was established. Results: The needs assessments identified a strong interest in educational and coaching opportunities. IMRT indication guidelines were developed for 10 disease sites and utilization targets were set for 6 disease sites. Results from two cost effectiveness analyses confirmed IMRT as a standard of care over 3D Conformal Radiation Therapy. 11 cancer centres received IMRT expert-based coaching, which included hands on-training, site visits, and educational support. Knowledge sharing and educational opportunities were provided to over 949 radiation therapists, oncologists and physicists. 13 cancer centres participated in external IMRT quality assurance testing for head and neck and prostate cancer. IMRT utilization has increased without affecting wait times or patient safety with no increase in incidents during this time period. All six disease sites have me their targets for 2012/13. Conclusions: IMRT knowledge and expertise across the province has grown along with a dramatic increase in the percentage of patients being treated for curative intent receiving IMRT. This jurisdictional implementation project was successful in making this treatment available to all Ontarians who would benefit. The lessons learned from this experience may be useful in the future rollout of other new technologies in Ontario and in other health care settings.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".