Cerebral Blood Flow Predicts Recovery in Children with Persistent Post-Concussion Symptoms after Mild Traumatic Brain Injury
Bibliographic record
Abstract
Persistent post-concussion symptoms (PPCS) following pediatric mild traumatic brain injury (mTBI) are associated with differential changes in cerebral blood flow (CBF). Given its potential as a therapeutic target, we examined CBF changes during recovery in children with PPCS. We hypothesized that CBF would decrease and that such decreases would mirror clinical recovery. In a prospective cohort study, 61 children and adolescents (mean age 14 [standard deviation = 2.6] years; 41% male) with PPCS were imaged with three-dimensional (3D) pseudo-continuous arterial spin-labelled (pCASL) magnetic resonance imaging (MRI) at 4–6 and 8–10 weeks post-injury. Exclusion criteria included any significant past medical history and/or previous concussion within the past 3 months. Twenty-three participants had clinically recovered at the time of the second scan. We found that relative and mean absolute CBF were higher in participants with poor recovery, 44.0 (95% confidence interval [CI]: 43.32, 44.67) than in those with good recovery, 42.19 (95% CI: 41.77, 42.60) mL/min/100 g gray tissue and decreased over time (β = -1.75; p < 0.001). The decrease was greater in those with good recovery (β = 2.29; p < 0.001) and predicted outcome in 77% of children with PPCS (odds ratio [OR] 0.54, 95% CI: 0.36, 0.80; p = 0.002). Future studies are warranted to validate the utility of CBF as a useful predictive biomarker of outcome in PPCS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".