Fracture Risk From Psychotropic Medications
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it