Improving radiotherapy utilization rates in Ontario.
Bibliographic record
Abstract
48 Background: Radiotherapy (RT) utilization is the proportion of patients (Pts) with a new diagnosis of carcinoma that receives at least one course of RT during the duration of their illness (MacKillop et al). In Ontario, the benchmark for utilization is 42%, and the target rate for Cancer Care Ontario (CCO) is 48%. However in 2005/06 Ontario’s utilization rate was only 35%. This shortfall implies that a significant number of Pts in Ontario were not receiving the best possible treatment. Methods: CCO has developed Capital Investments Strategies to take into account a growing population, an increased incidence of cancer (2.7% per annum), and changing demographics. The most recent (2012) has developed a plan for a gradual improvement in utilization at a rate of 0.5% per annum. With support from the Ministry of Health and Long-Term Care (MOHLTC), the amount of RT Units (RTUs) available increased from 77 (2005/06) to 103 (2013/14). In addition, the MOHLTC supported training programs for provincial Medical Physicists and Radiation Therapists to ensure effective staffing for increased RTUs. Results: Due to investments in RTUs and training, the number of Pts treated in Ontario has risen over 38% from 26,448 in 2005/06 to 36,613 in 2012/13. As a result, provincial utilization rates have risen from 35.1% in 2004/05 to 38.8% in 2012/13, closer to provincial targets. Notable improvements have been seen in centres such as The Carlo Fidani Peel Regional Cancer Centre, where the total number of RT Pts treated increased from 623 (2005/06) to 2532 (2012/13). Similar improvements are seen in other Local Health Integration Networks where comparable investments have been made. These improvements have been attained at the same time as provincial RT wait times have significantly improved from 80.2% (Jan 2010) to 91.6% (Dec 2012) of Pts seen within targets, exceeding provincial targets of 87%. Conclusions: The increase in RT utilization rates provincially demonstrates the success of developing a comprehensive capital investment strategy and coupling it with increased investments in human resource planning. The increased utilization rate has outpaced the increasing cancer incidence and demonstrates the success of these strategies, providing better access to care for cancer Pts in Ontario.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".