Benefits Outweigh Costs in Universal Healthcare: Business Case for Reimbursement of Take-home Cancer Medicines in Ontario and Atlantic Canada
Bibliographic record
Abstract
Cancer has the fastest growing prevalence of any non-communicable disease in Canada. Oral and other take-home cancer drugs have been a major game-changer allowing cancer patients to live longer while staying at home without the stressful ordeal of IV chemotherapy. The Canada Health Act only provides for government reimbursement for IV cancer drugs administered in a hospital or cancer centre. In Ontario and Atlantic Canada, patients must personally pay some or all of the cost of medications that are taken at home, even if they are considered the standard of care as part of internationally accepted treatment protocols. A sensitivity analysis was conducted for Ontario on the pool of 9,588 financially vulnerable new cancer patients assuming different levels of oral drug penetration and different drug costs. The last dollar scenario, where the Province steps in after private insurance has paid its share, for a year's worth of oral cancer drugs for new cancer cases would produce a budget impact of $28 million. For first-dollar coverage the budget impact would be $58.5 million. On an on-going, annualized, first-dollar basis, covering all cancer cases, full coverage would yield a budgetary impact of $93.8 million. Universal funding of oral cancer drugs will save the healthcare system money overall; provide better, more meaningful data; provide better quality of life for cancer patients; provide better purchaser negotiating positions for the procurement of novel prescription pharmaceuticals; and, provide quicker access for patients to life-saving therapy with better outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".