Governance of conflicts of interest in postmarketing surveillance research and the Canadian Drug Safety and Effectiveness Network.
Bibliographic record
Abstract
There is growing concern about the limited information on the long-term safety and effectiveness of many pharmaceutical products. Manufacturers of pharmaceutical products are currently not required to conduct post-marketing efficacy or safety studies of these products. In part to deal with growing concerns about the long-term safety and effectiveness of pharmaceutical products, the Canadian Federal Government recently provided funding for the establishment of a new Drug Safety and Effectiveness Network (DSEN). The DSEN was set up within the Canadian Institutes of Health Research, the major medical funding agency, with as its core mandate the promotion of postmarketing surveillance research. This paper, which builds on a discussion paper written for a policy forum organized prior to the establishment of the DSEN, explores the key governance standards and principles that ought to be respected by an agency that has as its mandate to promote post-marketing safety and effectiveness research. The paper discusses the need for stringent conflict of interest guidelines, and explores five intertwined principles that should be respected in an agency with this mandate: transparency and openness, accountability, independence, commitment to scientific integrity, and freedom of action. Compliance to these five principles, the paper argues, is essential for promoting reliable and independent post-marketing research and for re-establishing the public’s trust in postmarketing studies.
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 | 0.207 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.019 | 0.012 |
| 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".