Predicting Fatigue 12 Months after Child Traumatic Brain Injury: Child Factors and Postinjury Symptoms
Bibliographic record
Abstract
OBJECTIVES: Fatigue is a common and persisting symptom after childhood brain injury. This study examined whether child characteristics and symptomatology preinjury or 6 months postinjury (pain, sleep, and mood, inattention) predicted fatigue at 12months postinjury. METHODS: Parents of 79 children (0-18 years) rated fatigue at 12 months after injury on a multidimensional scale (general, sleep/rest, and cognitive). Demographic and clinical data were collected at injury. Parents rated child sleep, pain, physical/motor function, mood, and inattention at injury (preinjury description), and 6 months postinjury. Children were divided into two traumatic brain injury severity groups: mild TBI (n=57) and moderate/severe TBI (n=27). Hierarchical regression models were used to examine (i) preinjury factors and (ii) symptoms 6 months postinjury predictive of fatigue (general, sleep/rest, and cognitive) at 12 months postinjury. RESULTS: Sleep/rest fatigue was predicted by preinjury fatigue (7% of variance) and psychological symptoms preinjury (10% of variance). General fatigue was predicted by physical/motor symptoms (27%), sleep (10%) and mood symptoms (9%) 6 months postinjury. Sleep/rest fatigue was predicted by physical/motor symptoms (10%), sleep symptoms (13%) and mood symptoms (9%) 6 months postinjury. Cognitive fatigue was predicted by physical/motor symptoms (17%) 6 months postinjury. CONCLUSIONS: Preinjury fatigue and psychological functioning identified those at greatest risk of fatigue 12 months post-TBI. Predictors of specific fatigue domains at 12 months differed across each of the domains, although consistently included physical/motor function as well as sleep and mood symptoms postinjury. (JINS, 2018, 24, 224-236).
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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 | 0.000 | 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 | 0.001 |
| 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".