Postmarket surveillance: a review on key aspects and measures on the effective functioning in the context of the United Kingdom and Canada
Bibliographic record
Abstract
Regulatory approvals for the marketing of medicinal products authorize medical practitioners to prescribe drugs to a group of patients that are defined within the license of the medicinal product. However, such prescriptions are carried out in a controlled manner. Prior to being approved, the medicinal product will have been evaluated in a population pool containing fewer than 5,000 patients and in a predesigned environment where several factors may be lacking, such as the absence of women of childbearing potential, geriatric patients and paediatric patients. Therefore, it is not surprising that several major adverse drug reactions are detected only when the product has been prescribed to the general population. National and international regulatory bodies have devised systems for monitoring medicinal products after marketing, commonly known as postmarketing surveillance systems. Postmarketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. The primary purpose for conducting postmarketing surveillance is to identify previously unrecognized adverse effects as well as positive effects. The Yellow Card scheme, practiced in the United Kingdom and the Canada Vigilance Program adopted in the Canadian jurisdiction, are two of the most successful postmarketing surveillance systems implemented across the world. Therefore, this article intends to discuss postmarketing surveillance and its role in the context of the United Kingdom and Canadian jurisdictions with a view on presenting key aspects and measures that are employed for operating an efficient postmarketing surveillance system in regulated markets.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".