6.5 Task-specific changes in cerebral hemodynamics in pediatric mild traumatic brain injury
Bibliographic record
Abstract
Objective Emerging evidence suggests that advanced neuroimaging of cerebral hemodynamics may yield a clinical biomarker of mild traumatic brain injury (mTBI). We examined whether (coherence) and novel (absolute phase) functional near-infrared spectroscopy metrics differentiated children with mTBI from mild orthopedic injury (OI). Design Prospective cohort Participants Children between 8.00–16.99 years of age were recruited during emergency department visits at Alberta Children’s Hospital within 48 hours of sustaining an mTBI (n = 8, 62.5% sport-related concussion) or mild OI (n=5), and completed fNIRS as part of a follow-up assessment 2 months post-injury (M = 35.38±5.19 days). fNIRS was collected during rest and a 2-back working memory task, with optodes placed over four regions of interest (bilateral dorsolateral prefrontal and motor cortex). Outcome Measures Two-way ANOVA was used to evaluate the effect of group, regions of interest (ROI), and their interaction on coherence and absolute phase. Main Results During rest, children with mTBI demonstrated lower absolute phase across ROIs relative to children with OI (F(1, 82)=14.79, p<.001) but the groups did not differ in resting state coherence. During the 2-back task, coherence was reduced in children with mTBI relative to OI, (F(1, 82)=12.35,p<.001), and also varied across ROI, (F(5, 82)=3.82, p=0.004). Conclusions Our results suggest differences in cerebral hemodynamic measures in pediatric mTBI were context-dependent, with reduced absolute phase at rest and reduced coherence during working memory performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".