Fatigue Following Traumatic Brain Injury in Children and Adolescents: A Longitudinal Follow-Up 6 to 12 Months After Injury
Bibliographic record
Abstract
BACKGROUND: Longitudinal fatigue data in children suffering from traumatic brain injury (TBI) are lacking. OBJECTIVES: To examine the effects of time postinjury (6-12 months) and injury severity on fatigue after childhood TBI. Secondarily, we compared fatigue 12 months postinjury against published control data. SETTING: Three tertiary children's hospitals across Australia (n = 1) and Canada (n = 2). PARTICIPANTS: Parents (n = 109) of children (mean [M] = 9.9 years at injury; range, 1.0-16.9 years) admitted to one of 3 participating hospitals with mild (n = 69) or moderate/severe (n = 37) TBI. DESIGN: Longitudinal prospective study. MEASURES: Primary: Pediatric Quality of Life Multidimensional Fatigue Scale (total, general, sleep/rest, and cognitive), rated by parents 6 and 12 months postinjury. Secondary: Pediatric Injury Functional Outcome Scale (fatigue and sleep items, rated on recruitment and 6 and 12 months postinjury). Demographic and children data were collected at recruitment. RESULTS: Mixed-models analysis demonstrated nonsignificant effects of time (6 vs 12 months postinjury) on multidimensional fatigue scores. Cognitive fatigue worsened over time. Moderate/severe TBI was associated with worse fatigue 12 months postinjury (general, P = .03; cognitive, P = .02). Across all severities, fatigue 12 months postinjury was significantly worse compared with control data (total fatigue, P < .001; all domains, all Ps < .025). CONCLUSION: Fatigue remains significant at 12 months since injury, particularly for those with moderate/severe TBI.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".