Association of Antiepileptic Drugs With Nontraumatic Fractures
Bibliographic record
Abstract
OBJECTIVE: To explore the relationship between antiepileptic drug (AED) use and nontraumatic fractures in those aged 50 years and older. DESIGN: Retrospective matched cohort study. PARTICIPANTS: A total of 15,792 persons, identified through the Population Health Research Data Repository from Manitoba, Canada, with nontraumatic fractures of the wrist, hip, and vertebra occurring between 1996 and 2004. Each patient was matched for age, sex, ethnicity, and comorbidity with up to 3 controls (n = 47,289). INTERVENTIONS: Prior AED use (carbamazepine, clonazepam, ethosuximide, felbamate, gabapentin, lamotrigine, levetiracetam, oxcarbazepine, phenobarbital, phenytoin, pregabalin, primidone, topiramate, valproic acid, and vigabatrin) was determined from pharmacy data in the repository. Odds ratios (OR) for fracture from AED exposure were adjusted for sociodemographic and comorbidity factors known to affect fracture risk. RESULTS: A significant increase in fracture risk was found for most of the AEDs being investigated (carbamazepine, clonazepam, gabapentin, phenobarbital, and phenytoin). The adjusted ORs ranged from 1.24 (95% confidence interval [CI], 1.05-1.47) for clonazepam to 1.91 (95% CI, 1.58-2.30) for phenytoin. The only AED not associated with increased fracture risk was valproic acid (adjusted OR, 1.10; 95% CI, 0.70-1.72). CONCLUSIONS: Most AEDs were associated with an increased risk of nontraumatic fractures in individuals aged 50 years or older. Further studies are warranted to assess the risk of nontraumatic fractures with the newer AEDs and to determine the efficacy of osteoprotective medications in this population.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".