Sleep disturbances in children with epilepsy compared with their nearest‐aged siblings
Bibliographic record
Abstract
The aim of the study was to compare sleep patterns in children with epilepsy with those of their non‐epileptic siblings and to determine which epilepsy‐specific factors predict greater sleep disturbance. We conducted a case‐control study of 55 children with epilepsy (mean age 10y, range 4 to 16y; 27 males, 28 females) and their nearest‐aged non‐epileptic sibling (mean age 10y, range 4 to 18y; 26 males, 29 females). Epilepsy was idiopathic generalized in eight children (15%), symptomatic generalized in seven (13%), and focal in 40 (73%); the mean duration was 5 years 8 months. Parents or caregivers completed the Sleep Behavior Questionnaire (SBQ) and Child Behavior Checklist (CBCL) for patients and controls, and the Quality of Life in Childhood Epilepsy (QOLCE) for patients. Patients had a higher (more adverse) Total Sleep score (p<0.001) and scored worse than controls on nearly all subscales of the SBQ. In patients, higher Total Sleep scores were correlated with higher scores on the Withdrawn, Somatic complaints, Social problems, and Attention subscales of the CBCL, and significantly lower Total Quality of Life Scores. Refractory epilepsy, mental retardation, * and remote symptomatic etiology predicted greater sleep problems in those with epilepsy. We conclude that children with epilepsy in this current study had significantly greater sleep problems than their non‐epileptic siblings.
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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 | 0.001 | 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".