Bibliographic record
Abstract
Medication-related adverse events are a major cause of disability and death,1 and one of the most common reasons that patients attend hospital emergency departments.2 Much of this harm is pre-ventable, either because a less hazardous treatment is available, the medicine is not really needed, or it is inappropriate for this specific patient. Many initiatives exist to improve medi-cine use. Schiff et al3 call for a more judi-cious and precautionary approach to prescribing, with a focus on long-term as well as short-term health. To judge a medicine’s net benefit to a patient, pre-scribers need comprehensive, accurate information on potential harmful as well as beneficial effects. Given the import-ance of medicines in treatment, informa-tion on harm is surprisingly inconsistent and elusive. Approved product information describes adverse events experienced by patients in premarket studies as well as new safety signals once a drug is marketed. In their article, ‘Speaking the same language? International variations in the safety infor-mation accompanying top-selling prescrip-tion drugs’, Kesselheim et al4 describe differences in numbers and types of adverse events in product information for the same 20 top-selling medicines in the US, UK, Canada and Australia. There is no reason to suspect that
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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.005 |
| Insufficient payload (model declined to judge) | 0.913 | 0.625 |
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".