Enhancing the delivery of take-home cancer therapies in Ontario.
Bibliographic record
Abstract
46 Background: The delivery of systemic cancer therapy has expanded from primarily intravenous (IV) treatment, delivered in cancer centres, to include significant use of take-home cancer therapies (THCT) (e.g., oral medications). An industry pipeline survey suggests half of new cancer drugs are expect to be THCT. While IV treatments administered in hospitals are publicly funded for Ontario residents, public funding of THCT is dependent on age, socioeconomic status, and other factors. The delivery of these therapies may also take place outside the cancer centre. While the lack of universal funding is cited as a significant barrier to access, the growing use of THCT has introduced other system delivery questions. Cancer Care Ontario recently hosted a “Think Tank” to inform public policy recommendations for system change to enhance the delivery of THCT in the province. Methods: The day was highly interactive with health professionals and patient participants, and was structured around a case study of a patient receiving both IV and THCT. Approaches taken with THCT in other Canadian provinces were examined. Participants used a multi-dimensional framework (safety and quality; reimbursement and distribution; data and information) to develop recommendations across pre-defined “checkpoints” in the patient’s treatment journey. Pre-assigned groupings of participants with common professional/patient backgrounds developed recommendations that were subsequently prioritized by reassigned multidisciplinary groups. Results: Over 80 stakeholders developed and prioritized more than 180 recommendations. Major themes included education, technology levers, and drug access model reform. This advice will inform system planning and next steps in defining opportunities for system change. The majority of participants (84%) felt the event broadened their understanding of THCT delivery issues. Conclusions: The strategic design of the “Think Tank” facilitated the development of robust recommendations for improving the delivery and reimbursement of THCT. These recommendations, along with the lessons learned from other provinces, will provide the foundation for potential policy and system changes to enhance the quality of THCT delivery 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".