Funding New Cancer Drugs in Ontario: Closing the Loop in the Practice Guidelines Development Cycle
Bibliographic record
Abstract
PURPOSE: The previously described practice guidelines development cycle follows an iterative model in which recommendations are reached by a process that incorporates practitioners at all phases. A key feature is the separation of the evidence-based systematic review and the generation of recommendations from policy decisions surrounding implementation. This article describes how this implementation phase has evolved in Ontario and how implementation has affected the guidelines process. METHODS: The development of the New Drug Funding Program in Ontario and the appointment of a policy advisory committee (PAC) to make funding recommendations were reviewed. The decision-making framework of the PAC is described in this article. RESULTS: The PAC has had to address a number of issues in making funding recommendations. These issues have included dealing with evidence arising solely from phase II versus phase III trials, using economic information, and involving community representatives in its deliberations. Its activities have had a substantial impact on the practice guidelines initiative. CONCLUSION: It is possible to integrate an evidence-based, practitioner-driven approach to clinical guideline development with a funding program that takes policy considerations into account. However, even though these two roles are conceptually separate, the needs of the funding program have inevitably had an impact on the guidelines process.
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 | 0.215 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".