Antipsychotic drug exposure and risk of fracture
Bibliographic record
Abstract
To investigate the extent to which exposure to first-generation and second-generation antipsychotics (APs) is associated with an increased risk of fractures, with a particular focus on hip fractures, and to ascertain the risk associated with exposure to individual drugs. We included observational studies that reported data on fractures in individuals exposed to APs compared with unexposed individuals or individuals with previous exposure. We extracted information on study design, source of data, population characteristics, outcomes of interest, matching and confounding factors, and used a modified version of the Newcastle-Ottawa Scale to judge study risk of bias. We pooled adjusted estimates of relative effects to generate pooled odds ratios (ORs) and their 95% confidence interval (CI) using a random-effects model. We rated the quality of evidence using the GRADE approach. Of 36 observational studies, 29 proved to have a low risk of bias and seven were found to have a high risk of bias. The risk of hip fracture (OR: 1.57, 95% CI: 1.42-1.74, low quality of evidence) and of any fracture (OR: 1.17, 95% CI: 1.04-1.31, very low quality of evidence) increased with exposure to APs, with similar increases in risk in the first generation and second generation. The risk was similar among different diagnostic categories. The few studies that provided data were insufficient to allow inferences on individual drugs. AP exposure in unselected populations was associated with a 57% increase in the risk of hip fractures and a 17% increase in the risk of any fractures. Between-study heterogeneity limits the confidence in this estimate.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".