Bibliographic record
Abstract
OBJECTIVE: To examine the potential efficacy of hydroxymethyl-glutaryl-coenzyme A reductase inhibitors (statins) in the prevention of epilepsy. METHODS: This study was a population-based, nested case-control study among older adults in the province of Quebec, Canada. The primary cohort consisted of cardiovascular patients who had received a revascularization procedure. Within the cohort, those with the primary hospital diagnosis of epilepsy were identified (cases). Each case was matched to 10 controls by age and cohort entry time. Potential confounders were adjusted using a conditional logistic regression model. A sensitivity analysis was performed using propensity score matching. RESULTS: The initial cohort consisted of 150,555 subjects. Within the cohort, 217 hospital-diagnosed cases of epilepsy and 2,170 corresponding controls were identified. The adjusted rate ratio (ARR) for epilepsy among current statin users was 0.65 (95% confidence interval [CI] 0.46-0.92). The ARR for past users of statins was 0.72 (95% CI 0.39-1.30). No benefit was found for the control drug groups, including nonstatin cholesterol-lowering drugs, β-blockers, and angiotensin-converting enzyme inhibitors (1.00 [95% CI 0.45-2.20], 1.04 [95% CI 0.74-1.47], and 0.94 [95% CI 0.66-1.33]). CONCLUSIONS: These results suggest that statin use decreases the risk of hospitalization for epilepsy. Because of its observational nature, this study requires future research to confirm these intriguing findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".