The Importance of and Challenges with Adopting Life-Cycle Regulation and Reimbursement in Canada
Bibliographic record
Abstract
Regulatory and reimbursement decisions for drugs and vaccines are increasingly based on limited safety and efficacy evidence. In this environment, life-cycle approaches to evaluation are needed. A life-cycle approach grants market approval and/or positive reimbursement decisions based on an undertaking to conduct post-market clinical trials that address evidentiary uncertainties, relying on the collection and analysis of post-market data. In practice, however, both conditional regulatory and reimbursement decisions have proven problematic. Here we discuss some of the regulatory implications and unsettled ethical and pragmatic issues, taking lessons from the recent experiences of Israel in rapidly approving the Pfizer-BioNTech COVID-19 vaccine.
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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 | 0.114 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.026 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".