Quality of life and its association with comorbidities and adverse events from antiepileptic medications: Online survey of patients with epilepsy in Australia
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the quality of life (QoL) of adult patients with epilepsy (PwE) in Australia and its relationship with comorbidities and adverse events (AEs) from antiepileptic drugs (AEDs). METHODS: Cross-sectional surveys were completed by PwE, or carer proxies, recruited via the online pharmacy application MedAdvisor and Australian PwE Facebook groups from May to August 2018. Data were collected on demographics, epilepsy severity and management, AEs, comorbidities, and QoL (using the Patient-Weighted Quality of Life in Epilepsy Inventory [QOLIE-10-P] total score). Two linear regression models were constructed to explore associations between AEs or comorbidities and QOLIE-10-P score, with possible confounders determined using stepwise selection. RESULTS: Nine hundred and seventy-eight of 1267 responses were eligible (mean age of respondents: 44.5 years, 64% female, 52% employed). Recent AED use was reported by 97%; 47% were on AED monotherapy, 35% had ≤2 lifetime AEDs, and 55% were seizure-free for >1 year. After stepwise selection, control variables included in both models were time since diagnosis, employment status, seizure frequency, number of currently prescribed AEDs, and number of general practitioner (GP) visits per year. In the model for comorbidities, "psychiatric disorders" was associated with the largest QOLIE-10-P score decrease (-23.14, p < 0.001). In the model for AEs, which additionally controlled for depression and anxiety disorder, self-reported "memory problems" was associated with the largest decrease in QOLIE-10-P score (-14.27, p < 0.001). CONCLUSIONS: In this survey of Australian PwE, many of whom had relatively well-controlled epilepsy, psychiatric and self-reported memory problems were common and associated with the greatest detrimental impact on QoL. Further research is needed to better understand the underlying causes of impaired QoL and thereby improve its management.
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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 | 0.000 | 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 | 0.000 |
| 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".