P.091 Multi-modal analysis of outcomes in pediatric mild traumatic brain injury (mTBI)
Bibliographic record
Abstract
Background: mTBI is the most common type of head injury among children but diagnosing and managing symptoms are challenging due to heterogeneity. This study used multi-modal analysis to examine how acute mTBI symptoms transition to chronic deficits. Methods: Subjects included a cohort with mild TBI (n=40, 8-18 years old) and age/sex-matched controls (n=27). All participants received symptom assessment, neuropsychological evaluation, ERP assessment, neuroimaging, and serum cytokine analysis. Results were analyzed individually and in multi-modal models to identify important outcome predictors. Results: mTBI resulted in higher symptom burdens compared to controls. There were no group differences in measures of balance, ERP, FA, or MD. Female mTBI participants had lower CNSVS Neurocognition Index scores (p=0.0401) and faster reaction times (p=0.0385) than controls. Repetitive mTBI males had faster psychomotor speed than symptomatic mTBI males (p=0.0260). CTACK levels were higher in female mTBI groups (p= 0.0043), SCGF- levels were lower in male mTBI groups (p=0.0486), and MDC levels were lower in female mTBI groups (p=0.0377) compared to controls. Multi-modal models revealed key predictors from all modalities, despite most measures producing non-statistically significant results in individual analyses. Conclusions: Multi-modal analysis may afford the opportunity to delineate complex mTBI pathology and provide better identification of biomarkers than unimodal analysis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".