Assessments and Interventions for Sleep Disorders in Infants With or at High Risk for Cerebral Palsy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Children with cerebral palsy (CP) are five times more likely than typically developing children to have sleep problems, resulting in adverse outcomes for both children and their families. The purpose of this systematic review was to gather current evidence regarding assessments and interventions for sleep in children under age 2 years with or at high risk for CP and integrate these findings with parent preferences. METHODS: Five databases (CINAHL, EMBASE, OVID/Medline, SCOPUS, and PsycINFO) were searched. Included articles were screened using preferred reporting items for systematic reviews and meta-analyses guidelines, and quality of the evidence was reviewed using best evidence tools by two independent reviewers at minimum. An online survey was conducted regarding parent preferences through social media channels. RESULTS: Eleven articles met inclusion criteria. Polysomnography emerged as the only high-quality assessment for the population. Three interventions (medical cannabis, surgical interventions, and auditory, tactile, visual, and vestibular stimulations) were identified; however, each only had one study of effectiveness. The quality of evidence for polysomnography was moderate, while the quality and quantity of the evidence regarding interventions was low. Survey respondents indicated that sleep assessments and interventions are highly valued, with caregiver-provided interventions ranked as the most preferable. CONCLUSIONS: Further research is needed to validate affordable and feasible sleep assessments compared to polysomnography as the reference standard. In the absence of diagnosis-specific evidence of safety and efficacy of sleep interventions specific to young children with CP, it is conditionally recommended that clinicians follow guidelines for safe sleep interventions for typically developing children.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".