Predictors of Sleep Outcomes Following Mild Traumatic Brain Injury in Preschoolers: Subjective and Objective Assessment of Outcome
Bibliographic record
Abstract
OBJECTIVES: To investigate sleep and its predictors in preschoolers with mild traumatic brain injury (mTBI). PARTICIPANTS AND PROCEDURE: The sample included 225 children, aged 18 to 60 months, divided into 3 groups: children with accidental mTBI (n = 85), children with orthopedic injury (n = 58), and typically developing children (n = 82). Retrospective reports of preinjury sleep and family functioning were obtained as part of a baseline assessment at the time of recruitment. Parental ratings of sleep problems were collected 6 months postinjury on the full sample, and actigraphy data were collected on a subset of children. Demographic, preinjury, and periinjury factors were examined as potential predictors of sleep outcomes. RESULTS: No group differences were found in ratings of sleep problems or for nighttime sleep duration and sleep efficiency, as measured by actigraphy. Besides, preexisting sleep disturbances and brain injury resulting in alteration of consciousness were identified as predictors of poorer sleep in the mTBI group. CONCLUSION: Although mTBI did not result in group-level sleep disturbances 6 months postinjury, the findings suggest that premorbid and injury-related factors place some children at risk for poorer sleep after mTBI. These factors should be documented so clinicians can intervene early.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".