Sleep quality and daytime sleepiness in epilepsy: Systematic review and meta-analysis of 25 studies including 8,196 individuals
Bibliographic record
Abstract
We sought to gain a better understanding of the relationship between epilepsy and sleep quality and daytime sleepiness by performing a literature search of PubMed for case–control studies that compared patients with epilepsy to controls and reported the Pittsburgh sleep quality index (PSQI) and/or the Epworth sleepiness scale (ESS). Study-specific mean differences in the PSQI and ESS between cases and controls were extracted from the publications and pooled using random-effects meta-analysis. Twenty-five studies (2964 cases, 5232 controls) were included. Fifteen studies reported the PSQI and 24 the ESS. Mean age was 40 years; 50.4% were women. When comparing cases to controls, the pooled mean differences in the PSQI and ESS were 1.27 (95% confidence interval (CI): 0.76, 1.78; P < 0.001; I2: 81.4%) and 0.38 (95% CI: −0.07, 0.84; P = 0.099; I2: 81.0%). Subgroup analyses revealed that mean differences in the ESS were significantly lower in studies with a higher proportion of patients with focal epilepsy (P = 0.004). In this large-scale meta-analysis patients with epilepsy had a higher PSQI, close to the pathological cut-off, compared to controls, but a similar and unremarkable ESS. Further studies are needed to investigate potential effect modifiers, such as specific antiepileptic drugs or seizure frequency.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".