Association between antiseizure medications and quality of life in epilepsy: A mediation analysis
Bibliographic record
Abstract
Abstract Objective The relationship between antiseizure medications (ASMs), which improve health outcomes by controlling seizures, and health‐related quality of life (HRQOL) is poorly understood and may involve intermediate variables. We evaluated the potential mediators of the association between ASMs and HRQOL. Methods Data are from an outpatient registry of adult patients with epilepsy seen at the Foothills Medical Center, Calgary, Alberta, Canada. Quality of life was measured using the 10‐item Quality of Life in Epilepsy, and depression was measured using the Neurological Disorders Depression Inventory for Epilepsy. Propensity score matching was used to adjust for covariate imbalance between patients who received a single ASM (monotherapy) and those who received two or more ASMs (polytherapy) due to confounding. Mediation analysis was used to estimate the mediating effects of depression and ASM side effects on the association between patients' ASM polytherapy and HRQOL. Results Of 778 patients included in this analysis, 274 (35.2%) were on two or more ASMs. Patient‐reported depression and ASM side effects jointly mediated the association between ASMs and HRQOL; these mediators accounted for 42% of the total average effect of ASM polytherapy ( = −13.6, 95% confidence interval = −18.2 to −8.6) on HRQOL. Significance These findings highlight the importance of managing depression and ASM side effects for improving health outcomes of patients requiring treatment with ASMs. Intervention programs aimed at improving HRQOL of patients with epilepsy need to target these potential mediators.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".