0813 Increased Sleep Fragmentation and Emotional-Behavioral Problems in Toddlers Presenting Sleep Terrors
Bibliographic record
Abstract
Sleep terrors are nocturnal episodes characterized by screams, cries, and confusion lasting a few seconds to a few minutes. This parasomnia is especially prevalent in toddlers and has been associated with separation anxiety. While sleepiness and reduced sleep quality have been reported in adults with sleep terrors, it is unclear whether this holds true in children. The objectives of this study were to investigate whether sleep terrors were associated with sleep latency, duration and fragmentation, and emotional-behavioral problems. Participants were children from the Maternal Adversity Vulnerability and Neurodevelopment longitudinal cohort (N=509). Maternal reports were used to assess the presence or absence of sleep terrors and sleep habits (sleep latency, total nocturnal sleep duration, consecutive hours of sleep) when children were 12, 18, 24, and 36 months old. Internalizing and externalizing problems were assessed at 48 months with the Children Behavioral Checklist (maternal report). Sleep terrors were reported in 21% of 12 months old children, 16% of 18 months old, 20% of 24 months old, and 19% of 36 months old. Results from a generalized estimating equation model showed that, while controlling for total nocturnal sleep duration, the presence of sleep terrors in children was associated with longer sleep latency (p<0.05), and less consecutive hours of nocturnal sleep (p<0.05). Sleep terrors were also associated with increased internalizing and externalizing problems (p<0.05). The frequency of sleep terrors in our sample was similar to what is reported in the literature. Toddlers presenting sleep terrors had a more fragmented sleep, i.e. longer latency and less nocturnal sleep consolidation. It is not yet clear whether sleep terrors lead to increased sleep fragmentation, or whether they are triggered by fragmented sleep. Present results also suggest that sleep terror might represent an early sign of emotional-behavioral problems in toddlers. CIHR, Ludmer Centre for Neuroinformatics and Mental Health
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".