Fracture Risk From Psychotropic Medications
Bibliographic record
Abstract
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs), benzodiazepines, and antipsychotics have each been associated with an increased risk of fracture in older individuals. The aim of this study was to better define the magnitude of fracture risk with psychotropic medications and to determine whether a dose-effect relationship exists. METHODS: Population-based administrative databases were used to examine psychotropic medication exposure and fractures in persons aged 50 years and older in Manitoba between 1996 and 2004. Persons with osteoporotic fractures (vertebral, wrist, or hip [n = 15,792]) were compared with controls (3 controls for each case matched for age, sex, ethnicity, and comorbidity [n = 47,289]). Medications examined included antidepressants (SSRIs vs other monoamines), antipsychotics, lithium, and benzodiazepines. RESULTS: Selective serotonin reuptake inhibitors were associated with the highest adjusted odds of osteoporotic fractures (odds ratio [OR] = 1.45; 95% confidence interval [CI], 1.32-1.59). Other monoamine antidepressants (OR = 1.15; 95% CI, 1.07-1.24) and benzodiazepines (OR = 1.10; 95% CI, 1.04-1.16) were also associated with greater fracture risk, although the relationship was weaker. Lithium was associated with lower fracture risk (OR = 0.63; 95% CI, 0.43-0.93), whereas the relationship with antipsychotics was not significant in the models that adjusted for diagnoses. A dose-effect relationship was seen with SSRIs and benzodiazepines. CONCLUSIONS: This study provides novel insight into the relationship between fractures and psychotropic medications in the elderly. Selective serotonin reuptake inhibitors seem to have a greater risk than other psychotropic classes, and higher doses may further increase that risk. Lithium seems to be protective against fractures.
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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 | 0.003 |
| 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 | 0.000 | 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".