Improving cancer surgery in Ontario: recommendations from a strategic planning retreat.
Bibliographic record
Abstract
INTRODUCTION: The Ministry of Health and Long-Term Care mandated a rapid and thorough change in the delivery of cancer services in Ontario to integrate ambulatory services offered by Cancer Care Ontario (CCO) with the inpatient services of affiliated hospitals. The CCO Surgical Oncology Program held a strategic planning retreat to establish the basis upon which to implement surgery-specific changes. METHODS: Participants completed a pre-retreat survey. Based on survey results, the retreat was organized around 4 themes: role of the Surgical Oncology Program; knowledge transfer; funding for cancer surgery; and research priorities. These topics were discussed in small breakout groups and by the entire assembly. RESULTS: Retreat participants (n = 55) included hospital CEOs, vice-presidents of cancer services, surgeons from cancer centres and community hospitals, academic chairs of surgery, clinician researchers and managers from CCO. Responses to the pre-retreat survey (n = 38) and recommendations made by retreat participants showed strong support for the Surgical Oncology Program to take a leadership role in the development and monitoring of quality indicators, research related to cancer surgery and the creation of regional communities of practice. Funding mechanisms for cancer surgeons and hospitals performing cancer surgery were also highlighted. CONCLUSION: The Surgical Oncology Program used the results to develop a strategic plan that was approved by retreat participants and the board of the CCO. The program has embarked on a multifaceted approach to facilitate, monitor and report on the organization and delivery of cancer surgery 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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".