Exposure to Tricyclic and Selective Serotonin Reuptake Inhibitor Antidepressants and the Risk of Hip Fracture
Bibliographic record
Abstract
The use of tricyclic antidepressants is associated with an increased risk of hip fracture. Despite a better side effect profile, this adverse effect has also been reported for selective serotonin reuptake inhibitors. To determine whether these findings result from bias arising from the case-control method, the authors have performed a case-control analysis and a self-controlled case-series analysis using 1987-1999 diagnosis data for 16,341 cases of hip fracture and 29,889 controls drawn from the United Kingdom General Practice Research Database. Both analyses showed an association between hip fracture and antidepressant treatment, and this was most marked during the first 15 days of treatment. The estimates from the case-control study were larger than those from the case-series analysis: The odds ratios for fracture within the first 15 days of a prescription for tricyclic antidepressants and serotonin reuptake inhibitors were 4.76 (95% confidence interval (CI): 3.06, 7.41) and 6.30 (95% CI: 2.65, 14.97), whereas the equivalent incidence ratios were 2.30 (95% CI: 1.82, 2.90) and 1.96 (95% CI: 1.35, 2.83). Tricyclic antidepressants and serotonin reuptake inhibitors are both associated with an independent increase in hip fracture incidence during the first weeks of treatment. The estimates from the case-series analyses were smaller than those from the case-control analyses, suggesting that the case-control method is subject to bias.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".