Quality and quantity of data used by Health Canada in approving new drugs
Bibliographic record
Abstract
Background: This study examined multiple aspects about the approval of new drugs: the characteristics of the drugs, the quality and quantity of information that Health Canada discloses about the demographics of patients enrolled in clinical trials, the characteristics of the trial, and the type of review that it uses. It examines whether there have been changes in these measures between 1 September 2012 and 31 March 2022. Methods: A list of all new drugs approved, type of review used, and drug characteristics was generated from Health Canada annual reports. Therapeutic categories were identified from the World Health Organization Collaborating Center for Drugs Statistics Methodology. The Summary Basis of Decision documents of Health Canada were used to identify patient demographics in clinical trials and clinical trial characteristics. Results: = 0.005). There was also a statistically significant decrease in the percent of trials that were randomized, controlled, and blinded. The clinical trial characteristics of orphan drugs and the type of review used were both significantly different compared with non-orphan drugs. The percent of trials which had information about the number of patients enrolled, the percent of trials that provided the age of the patients, and the sex breakdown all significantly increased. Conclusion: The results show that there has been a change in regulatory standards that may be due to them becoming less rigorous, because of an adaptation to the number of orphan drugs being submitted or a combination of both reasons. At the same time, there has been some improvement in the transparency of data. Health Canada has recently embarked on a series of reforms in drug regulation and clinical trial management. These changes need to be closely evaluated to be sure that they enhance the efficacy and safety of new 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 | 0.544 | 0.782 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.026 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".