Closing the Gaps to Timely Patient Access: Perspectives on Conditional Funding Models
Bibliographic record
Abstract
The Canadian system for approval of new cancer drugs is complex with multiple steps. Health Canada grants a license for a drug to be marketed and prescribed. The Canadian Agency for Drugs and Technologies in Health (CADTH) and Institut national d'excellence en santé et services sociaux (INESSS) make recommendations by way of health technology assessments (HTA). If positive, the latter then lead to confidential price negotiations at the pan-Canadian pharmaceutical alliance (pCPA), after which individual provinces and territories make a listing decision. Delays can occur at each stage, but post-HTA delays can be lengthy and unpredictable, denying or impeding access to an effective drug with the potential for devastating clinical outcomes. Conditional funding models have been adopted in a number of European countries with the goal of providing timely access to new medications in areas of unmet need, in advance of further steps in the reimbursement process. This manuscript discusses different stakeholder perspectives on conditional funding agreements-including a recent successful example of such a process in the UK-based on a panel discussion at the 2021 Canadian Association of Population Therapeutics (CAPT) Conference.
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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 | 0.045 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 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".