Pre-injury sleep disturbance as a moderator of cognitive functioning in children and adolescents with mild traumatic brain injury
Bibliographic record
Abstract
OBJECTIVE: Healthy sleep contributes to better cognitive functioning in children. This study sought to investigate the role of pre-injury sleep disturbance as a predictor or moderator of cognitive functioning across 6 months post-injury in children with mild traumatic brain injury (mTBI) or orthopedic injury (OI). METHOD: Participants were 143 children with mTBI and 74 with OI, aged 8 - 16 years, prospectively recruited from the Emergency Departments of two children's hospitals in Ohio, USA. Parents rated their children's pre-injury sleep retrospectively using the Sleep Disorders Inventory for Students. Children completed the National Institutes of Health (NIH) Toolbox Cognition Battery at 10 days and 3 and 6 months post-injury. RESULTS: Group differences in both overall performance and reaction time on the Flanker Inhibitory Control and Attention Test varied significantly as a function of the level of pre-injury sleep disturbance as well as time since injury. At the 10 day visit, among children with worse pre-injury sleep, mTBI was associated with slower reaction times relative to OI. Among children with worse pre-injury sleep, those with mTBI improved over time while those with OI did not. Main effects of pre-injury sleep and time since injury were found for several other NIH Toolbox subtests, with poorer performance associated with worse pre-injury sleep and early vs. later timepoints. CONCLUSIONS: These results suggest that pre-existing sleep disturbances and mTBI are jointly associated with poorer executive functioning post-injury. Interventions to improve sleep might help mitigate the effects of mTBI on children's cognitive functioning.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".