Investigating the Statistical and Policy Frameworks Used to Gauge Potential Pharmacotherapy Recalls: A Scoping Review
Bibliographic record
Abstract
Paramount attention is often afforded to pharmacotherapies being brought to market. The anticipation from companies, their shareholders, and the patients poised to benefit are met with stiff statistical and methodological requirements set forth by Health Canada’s Health Products and Food Branch (HPFB). Once a pharmacotherapy has been approved by the HPFB, however, the onus to monitor these drugs falls largely on the shoulders of Health Canada, the manufacturers, and to a lesser degree, the Drug Safety and Effectiveness Network (DSEN). This scoping review identified existing pharmacovigilance frameworks recommendations from SCOPUS, PubMed and EMBASE and compared the guidelines employed by Health Canada, the US FDA, and the European Union Medicines Agency. The review found that the current system disproportionately relies on manufacturers to “self-regulate” and passively report adverse reactions, posing an obvious conflict of interest as to the safety of pharmacotherapies. As well, there remains a lack of clarity from Health Canada regarding their statistical methods and vision for improving risk mitigation strategies for post-market surveillance. This lack of centralization and standardization in the recall process lends itself to a suboptimal piecemeal solution meant to protect Canadians.
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 | 0.294 | 0.659 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.049 | 0.040 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".