Decreasing the Data Deficit: Improving Post-Market Surveillance in Pharmaceutical Regulation
Bibliographic record
Abstract
The drug regulatory system is currently largely based on market-entry review of safety and efficacy data and involves only very limited post-market review. Failures in the industry-controlled production of pre-market data and the lack of solid post-market surveillance contribute significantly to highly problematic drug prescription and consumption practices, which have become a very serious public health concern. In this paper, we first discuss how historically grown drug regulations have contributed to the development of industry control over clinical trials, which is one of the key factors behind the limits of pre-market evidence. We then explore some problematic aspects related to the fixation of the drug approval system on pre-market activities, including the lack of good “real-world” evidence on drug safety; the lack of comparative evidence on patient benefit between different drugs; the lack of evidence of the safety and efficacy of off-label prescribed drugs; and the inadequate reporting of adverse drug reactions (ADRs). We argue that a more rigorous and intense post-market surveillance system could counterbalance, at least in part, the distorted situation created by the regulatory reliance on pre-market, industry-produced clinical trials data. In particular, we advocate for improvements to the current ADR reporting system, more explicit requirements for both comparative effectiveness studies and post-market clinical research in real-world settings, the promotion of transparency of pharmaceutical data, and insulating clinical research from industry control.
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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 | 0.574 | 0.668 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.012 | 0.022 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".