Sleep characteristics in children, adolescents, and adults with Specific Learning Disorders: a systematic review and a meta-analysis
Bibliographic record
Abstract
Sleep is a fundamental psychophysiological process throughout lifespan, playing a crucial role in memory consolidation and learning. Sleep disturbances are highly prevalent among individuals with neurodevelopmental disorders; however, sleep characteristics and patterns in those with Specific Learning Disorders (SLDs) have been largely overlooked, and a comprehensive systematically synthesis lacks. This systematic review and meta-analysis (PROSPERO ID: CRD42021257350) aims to assess differences in sleep characteristics between individuals with SLDs and controls or individuals with neurodevelopmental disorders other than SLDs. Multiple search strategies identified a total of 13 independent studies, including case-control and cross-sectional designs, with a total sample of 695 children and adolescents and 55 adults with SLDs, and 7459 children and adolescents and 55 adults without SLDs. The risk of bias was evaluated using the Newcastle-Ottawa Scale for Assessing the Quality of Nonrandomized Studies in Meta-Analysis. Findings were synthesized narratively and through meta-analysis for both Objective and Subjective sleep outcomes. Meta-analyses of several Objective sleep macroarchitecture parameters showed no difference between groups. Conversely, children and adolescents with SLDs exhibited a significantly higher prevalence of sleep disturbances measured as Subjective outcomes compared to controls without SLDs. This study identifies gaps in literature and outlines priorities for future research. • No differences emerged in sleep macroarchitecture (e.g. Slow Wave and REM Sleep). • Sleep microarchitecture features differed between SLDs and control groups. • Individuals with SLDs present significantly more sleep disturbances than controls. • Both biological and environmental factors may underlie these alterations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".