‘Drugs to avoid’: can we improve prescribing appropriateness?
Bibliographic record
Abstract
Many initiatives to guide prescribing focus only on ‘drugs to use’ and not those to avoid. These include treatment guidelines, formularies, and national and regional lists of reimbursed drugs. The aim is to guide clinicians towards the most effective treatments and the most cost-effective of equivalent alternatives. However, this guidance can be incomplete, distorted by commercial interests1 and does not always adequately address harmful outcomes of medicines use. Criteria targeting inappropriate use also often focus on single drug classes such as opioids or specific demographics such as older patients for the Beers and STOPP/START criteria. The French independent drug bulletin Prescrire has developed a unique initiative aiming to improve prescribing appropriateness: an annual list of ‘drugs to avoid’ across all treatment indications and patient populations. These lists are based on Prescrire’s evaluations …
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".