Postmarket surveillance: a review on key aspects and measures on the effective functioning in the context of the United Kingdom and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it