Development of a capital investment strategy for radiation (RT) equipment in Ontario.
Bibliographic record
Abstract
279 Background: To ensure appropriate access to radiation treatment (RT) for Ontario cancer patients for the next decade and that future capital investments in radiation equipment are appropriately timed and strategically placed, Cancer Care Ontario (CCO) has updated its RT Capital Investment Strategy. The strategy was designed around 4 core principles: i) recognizing treatment machine capacity should match the demand resulting from increasing cancer incidence rates and increasing utilization rates as per CCO goals; ii) keeping pace with advancing technology; iii) ensuring value for money by maximising the use of current infrastructure; and iv) minimizing costs through centralized planning and procurement processes. Methods: A multidisciplinary provincial expert panel reviewed and revised the planning parameters used to project treatment demand and required capacity (including fractions of RT per treated case, number of cases treated per hour, uptime of treatment units). The panel reviewed current practice, impact of new and emerging treatment technologies and benchmarks from other jurisdictions. To project the future demand for radiation therapy, growth in cancer incidence (by county) as well as modest improvement in RT utilization rates were assumed. Results: Recommendations included: i) moving to 12-hour treatment days in all large centres and on 50% of equipment in centres operating fewer than 6 treatment units; ii) ensuring appropriate funding for the replacement of existing RT equipment; iii) equipping constructed rooms in 4 regional cancer centers – thereby adding 6 linacs; iv) equipping swing bunkers across the province – thereby adding 10 linacs; and v) planning for the construction of new facilities to add RT capacity in 3 regions of the province. Conclusions: Funding to implement recommendations from previous capital investment strategies has resulted in an equitable distribution of RT resources across the province. We believe the planning strategies and recommendations outlined in the strategy will improve access to quality RT care as close to home as feasible for Ontario patients.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".