Reimbursement recommendations before and after adoption of application fees by the Canadian Agency for Drugs and Technologies in Health: a cross-sectional study
Bibliographic record
Abstract
Abstract Objectives To determine if the introduction of drug company payment of application fees to the Canadian Agency for Drugs and Technologies in Health (CADTH) had an effect on its reimbursement recommendations to public drug funders for drugs with oncology and non-oncology indications. Methods Drug submissions from 2009 to 2019 (non-oncology drugs) and from 2012 to 2020 (oncology drugs) were analyzed and the CADTH recommendation (reimburse/do not reimburse) was recorded. Drugs indications were categorized as either oncology or non-oncology. Seven covariates that might have affected CADTH’s recommendations were entered into a logistic regression equation and the change in the odds ratio (OR) for recommending reimbursement after the introduction of application fees for both groups of drugs was computed. Key findings CADTH made recommendations for 258 drugs. After the introduction of application fees, there was a 0.8333 (95% CI 0.1640, 3.374) change in the OR of recommending reimbursement versus no reimbursement for drugs with an oncology indication. For drugs with a non-oncology indication, there was a 6.096 (95% CI 2.943, 13.39) change in the OR. Conclusions Industry funding of CADTH creates a conflict of interest that may have changed its recommendations for reimbursement for non-oncology drugs.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".