Bibliographic record
Abstract
Background: In 1995, Health Canada started collecting fees from pharmaceutical companies for various drug regulatory activities. This study investigated whether this change in the source of revenue affected the postmarket safety of drugs. Methods: A list of all new active substances approved in the 5 years before (1990–1994) and after (1995–1999) the introduction of cost recovery was compiled. Drug safety warnings and market withdrawals due to safety reasons were identified from the Health Canada Web site and other sources. Information about total funding for Health Canada’s drug regulatory program came from a report by KPMG, a global professional service company providing financial audit, tax and advisory services. Time-series analyses and Kaplan–Meier curves were used to determine whether cost recovery affected postmarket safety. Results: The introduction of cost recovery made no difference in the proportion of new active substances that received a safety warning, the number of safety warnings per new active substance or the time until the first safety warning or the likelihood that a drug would have a safety problem. Median drug review times decreased significantly after cost recovery was implemented (p = 0.02). Interpretation: The introduction of cost recovery and the associated reduction in review times did not affect the postmarket safety of drugs. Further changes to cost recovery, as are currently being proposed by Health Canada, need to be evaluated for any potential effects on the approval process that might influence decisions that Health Canada makes about the safety and efficacy of new drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".