Coverage with Evidence Development for Pharmaceuticals: A Policy in Evolution?
Bibliographic record
Abstract
Coverage with evidence development (CED) has been developed as a response to the uncertainty in evidence when new technologies, including pharmaceuticals, are introduced into the market. Rather than deny coverage for these technologies or grant them unlimited coverage, CED attempts to ensure that patients' access to new medications is not prevented but is managed in a coordinated way, while also generating additional evidence to reduce any uncertainty about the value of the medications. CED projects are currently operating in Australia, Canada, the United Kingdom, and the United States. However, decision-making about these projects is haphazard, and basic information about the projects is not publicly available. As a new policy development there are many unanswered policy questions, and no organized comprehensive strategy seems to be in place in any country for resolving these questions. Until these policy issues have been addressed, CED will have difficulty achieving its potential.
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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 | 0.151 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.054 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".