The Capital Investment Strategy for Radiation therapy in Ontario: A Framework to Ensure Access to Radiation Therapy
Bibliographic record
Abstract
PURPOSE: Ontario Health (Cancer Care Ontario), formerly known as CCO, is the provincial governmental agency in Ontario, Canada responsible for developing radiation therapy-specific capital investment strategies, updated every 5 years, to ensure equitable access and to gain the highest value from these investments in infrastructure. These plans are informed by the changing landscape of health care delivery, technologic advancements affecting radiation therapy care, patient desire for care closer to home, and expected increases in utilization of radiation therapy services. In this article, we describe the development, model, and final recommendations of CCO's fifth radiation therapy capital investment strategy. METHODS AND MATERIALS: A panel of multidisciplinary provincial experts, in combination with 2 patient and family advisors, developed planning principles to guide the development of a patient-centered strategy. Adaption of the previously used model for radiation therapy planning was used. RESULTS: The development of the capital investment strategy took place from fall 2017 to fall 2018. The model included 3 main factors: patient demand (including utilization targets), machine throughput, and machine demand and supply. The final recommendation is for an investment of 26 new radiation therapy machines in the province by 2028. CONCLUSIONS: The strategy plans for continued province-wide access to quality radiation therapy care and ensures machines are added to the system at the right place and in the right time. Ongoing data collection throughout this period is necessary to ensure the strategy achieves its goals and to allow for planning of future strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".