Bibliographic record
Abstract
The review by Borysowski et al does not deal with all of the issues surrounding off-label use in France.1 Indeed, Borysowski et al agree that the medical professional codes that they reviewed do not adequately address the problem of how new and innovative drugs are used.1 Finally, off-label use is only one way that the quality of prescribing is being compromised. On one hand, more and more drugs are granted accelerated approval by regulatory agencies on the basis of surrogate outcomes (with tenuous or unknown links for relevant clinical outcomes such as morbidity and quality of life) and on the promise of post-marketing studies which are only performed in two thirds of cases, and with a median delay of four years.12 On the other hand, market withdrawal is often unreasonably delayed, even in the case of drug-related deaths.13 Finding a prudent, middle ground to protect patient interests is an ongoing dilemma for drug regulators and prescribers, but moving too fast with approvals or prescriptions and too slow for withdrawals is the wrong path to follow. A.B. is a member of several task forces at the French Medicines Agency (Agence Nationale de Sécurité du Medicament). J.L. was a paid consultant on indication-based prescribing (United States Agency for Healthcare Research and Quality) and received payment for being on a panel that discussed a pharmacare plan for Canada (Canadian Institute, a for-profit organization). He is currently a member of The Jean Monnet Network in Health Law and Policy funded by the European Union (http://jmhealthnet.org/).
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 | 0.066 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".