Anti-Epileptic Medication Exposure Influences Functional Status in New Zealand Stroke Patients: A Retrospective Population-Level Study
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Bibliographic record
Abstract
BACKGROUND: Patients who develop seizures after stroke have disproportionately poorer outcomes and increased mortality. OBJECTIVE: Our objective was to investigate whether exposure to anti-epileptic medications influenced long-term functional status after stroke. METHODS: We used linked health administrative data from a cohort of adult stroke patients in New Zealand. Demographics and prescription information were obtained from the National Minimum Dataset and Pharmaceutical Collection, respectively. Activities of daily living (ADL) scores for the same patients were obtained using the International Resident Assessment Instrument. Beta regression was used to investigate the relationship between anti-epileptic drug (AED) exposure and functional status. RESULTS: The study included 3606 patients with a single ischaemic stroke between 2012 and 2017. In total, 15% were dispensed an AED in the 3 months before or after stroke. The adjusted odds ratio (OR) for AED exposure was 1.29 (95% confidence interval [CI] 1.15-1.45). Overall AED exposure, categorical body mass index (BMI), ethnicity, length of hospital stay, and exposure to paracetamol, opioids, anti-psychotics, and anti-nausea medications were significantly associated with changes in the mean ADL score percentages. Considering the exposure timeframe, the ORs for AED exposure only after stroke and for exposure both before and after stroke were 1.52 (95% CI 1.31-1.78) and 1.09 (95% CI 0.93-1.27), respectively. CONCLUSION: Stroke patients with AED exposure had greater odds of a higher ADL score, indicating a poorer long-term functional status than those unexposed to AEDs. The timeframe of exposure impacted on functional status, with patients exposed only after stroke having increased odds of higher ADL scores than those exposed both before and after stroke.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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