Interleukin-8 Predicts Fatigue at 12 Months Post-Injury in Children with Traumatic Brain Injury
Bibliographic record
Abstract
Despite many children experiencing fatigue after childhood brain injury, little is known about the predictors of this complaint. To date, traditional indices of traumatic brain injury (TBI) severity have not predicted reliably persisting fatigue (up to three years post-injury). This study aimed to establish whether persisting fatigue is predicted by serum biomarker concentrations in child TBI. We examined whether acute serum biomarker expression would improve prediction models of 12-month fatigue based on injury severity. Blood samples were collected from 87 children (1–17 years at injury) sustaining mild to severe TBI (Glasgow Coma Scale [GCS] range 3–15; mean 12.43; classified as mild TBI [ n = 50, 57%] vs. moderate/severe TBI [ n = 37, 43%]), and presenting to the emergency departments (ED) and pediatric intensive care units (PICU) at one of three tertiary pediatric hospitals (Royal Children's Hospital (RCH); Hospital for Sick Children (HSC), Toronto; St Justine Children's Hospital (SJH), Montreal). Six serum biomarker concentrations were measured within 24 h of injury (interleukin-6, interleukin-8 [IL-8], soluble vascular cell adhesion molecule [SVCAM], S100 calcium binding protein B [S100B], neuron specific enolase [NSE], and soluble neural cell adhesion molecule [sNCAM]). Fatigue at 12 months post-injury was measured using the Pediatric Quality of Life Inventory Multidimensional Fatigue Scale (parent report), classified as present/absent using previously derived cut-points. At 12 months post-injury, 22% of participants experienced fatigue. A model including IL-8 was the best serum biomarker for estimating the probability of children experiencing fatigue at 12 months post-injury. The IL-8 also significantly improved predictive models of fatigue based on severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".