Sleep and sleepiness in children with attention deficit / hyperactivity disorder and controls
Bibliographic record
Abstract
The present study assessed the association between habitual sleep patterns and one night of PSG measured sleep with daytime sleepiness in children with ADHD and typically developing children. Eighty-two children (26 ADHD, 56 typically developing children), between 7 and 11 years, had nighttime sleep recorded using actigraphy over five nights (habitual sleep patterns) and polysomnography during one night (immediate sleep patterns), both within their home environments. Daytime sleepiness was examined using the multiple sleep latency test within a controlled laboratory setting the following day. Using Spearman correlations, the relationships between mean sleep latencies on the multiple sleep latency test and scores on a modified Epworth Sleepiness Scale with polysomnographic measures of sleep quality and architecture and with actigraphic sleep quality measures were examined. Longer sleep latency, measured using polysomnography and actigraphy, was related to longer mean sleep latencies on the multiple sleep latency test in typically developing participants, whereas actigraphic measures of sleep restlessness (time awake and activity during the night), as well as time in slow-wave sleep, were positively related to mean sleep latency on the multiple sleep latency test in children with ADHD. These results show a differential relationship for children with ADHD and typically developing children between habitual and immediate sleep patterns with daytime sleepiness and suggest that problems initiating and maintaining sleep may be present both in nighttime and daytime sleep.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".