Multi-modal assessment of outcomes in pediatric mild traumatic brain injury
Bibliographic record
Abstract
Objective: Mild traumatic brain injury is the most common type of head injury among children and adolescents. Symptoms are highly heterogeneous and are affected by several factors pre- and post-injury, making diagnosis and symptom management challenging. This study aims to look at using multi-modal analysis to determine relationships between symptoms and contribute to the understanding of how acute symptoms transition to a chronic pathology. Study design: Participants aged 8-18 years and with a medically diagnosed mTBI were recruited through the Alberta Children’s Hospital Brain Injury Clinic. Age- and sex-matched healthy controls were recruited through word of mouth, sibling matches, and the Healthy Infants and Children's Clinical Research Program (HICCUP). Participants were categorized as follows: 1) symptomatic mTBI (n=26), 2) repetitive mTBI (n=14), and controls (n=27). Study appointments occurred within 6-16 weeks from the initial injury where the following assessments were administered: symptom assessment, balance assessment, neuropsychological evaluation, ERP assessment, neuroimaging, and blood collection for cytokine analysis. Each assessment was analyzed individually, and then used to build a multi-modal elastic-net regression model to identify significant predictors among all of the outcomes. Results: mTBI participants had higher PCSI scores and lower PedsQL scores compared to controls, indicating higher symptom burden. The mTBI groups had more difficulties with school, as reflected by the BASC assessment. No differences were found between groups for the balance assessment or the ERP assessment. On the CNSVS test, female mTBI participants had lower Neurocognition Index scores and faster reaction times compared to female controls. Repetitive mTBI males had faster psychomotor speed compared to symptomatic mTBI males. CTACK levels were elevated in female mTBI groups, SCGF-B levels were lower in male mTBI groups, and MDC levels were lower in female mTBI groups when compared to controls. No differences in FA and MD scores were found between groups and sexes scores for the left and right CPC tracts. Multi-modal assessment revealed important model predictors from the neuropsychology and cytokine assay modalities. Conclusion: Multi-modal assessment is a necessary tool to understand the key factors involved in the secondary injury and symptoms that arise after a pediatric mTBI. It is more informative than looking at individual assessments and highlights the limitations of looking at specific biomarkers in isolation when studying a complex injury affecting multiple systems in the body.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".