Research: the Janus Face of Regulation.” Competing interests: None declared.
Bibliographic record
Abstract
There has been surprisingly little research in the public domain on the long-term safety and effectiveness of postmarket drugs—drugs that have been approved by regulators and are being used by consumers—even though this information is needed by regulators, policy-makers, health care providers and consumers. The establishment in Canada of the national Drug Safety and Effectiveness Network (DSEN) 1 and its first call for applications for postmarketing studies are therefore positive developments for consumer and patient safety. Established and largely administered by the Canadian Institutes of Health Research (CIHR), the DSEN will fund independent and scientifically rigorous real-world studies on the safety and effectiveness of postmarket drugs in Canada and will connect to the research
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 | 0.125 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".