Need for an improved submission process for listing drugs for reimbursement in Canadian provinces.
Bibliographic record
Abstract
BACKGROUND: To have a drug listed in a province's formulary, manufacturers must submit an application providing data required by the provincial rules and guidelines. The procedures for the scientific evaluation of drugs considered for listing for reimbursement in five provinces have been examined previously. OBJECTIVE: The present study investigates the clarity of the same five provinces' rules and guidelines about effectiveness and cost data that should be included in listing submissions from the perspective of the pharmaceutical company. METHODS: The manufacturers of five recently introduced drugs selected by the investigators received questionnaires asking about the departments within their companies that are responsible for the submission, the data required by each of the five provinces and the clarity of each province's requirements for submission. Each company was also asked similar questions about its own submission experience with its drug. Investigators visited each manufacturer to review the questionnaires and answer questions. RESULTS: The manufacturers perceived the rules and guidelines on effectiveness and economic data of several provinces as being neither clear nor consistent. Consequently, information that companies submit in their attempts at compliance with the rules and guidelines varies substantially. CONCLUSIONS: The manufacturers' perceptions of the information required by the provinces on effectiveness and cost information were inconsistent. Previous work indicated that the provinces make significant decisions about listings based on inadequate information resulting in a scientifically flawed system that contributes to considerable inequality in access to new drugs between provinces. The findings of the present work reinforce this conclusion.
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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 | 0.096 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".