Effect of Selective Serotonin Reuptake Inhibitors on the Risk of Fracture
Bibliographic record
Abstract
BACKGROUND: Depression and osteoporotic fractures are common ailments among elderly persons. Selective serotonin reuptake inhibitors (SSRIs) are frequently used in the treatment of depression in this population, and the association between daily SSRI use and fragility fractures is unclear. Our objective was to examine the effect of daily SSRI use on the risk of incident clinical fragility fracture. METHODS: A population-based, randomly selected, prospective cohort study of 5008 community-dwelling adults 50 years and older, followed up over 5 years for incident fractures. Clinical fragility fractures were classified as minimal trauma fractures that were clinically reported and radiographically confirmed. The risk of fragility fracture associated with daily SSRI use was determined while controlling for relevant covariates. RESULTS: Daily SSRI use was reported by 137 subjects. After adjustment for many potential covariates, daily SSRI use was associated with substantially increased risk of incident clinical fragility fracture (hazard rate, 2.1; 95% confidence interval, 1.3-3.4). Daily SSRI use was also associated with increased odds of falling (odds ratio, 2.2; 95% confidence interval, 1.4-3.5), lower bone mineral density at the hip, and a trend toward lower bone mineral density at the spine. These effects were dose dependent and were similar for those who reported taking SSRIs at baseline and at 5 years' follow-up. CONCLUSIONS: Daily SSRI use in adults 50 years and older remained associated with a 2-fold increased risk of clinical fragility fracture after adjustment for potential covariates. Depression and fragility fractures are common in this age group, and the elevated risk attributed to daily SSRI use may have important public health consequences.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".