Acute sport-related concussion induces transient impairment in dynamic cerebral auto regulation that is related to scat3 performance
Bibliographic record
Abstract
Objective Examine how the frequency-dependent relationship between blood pressure (BP) and cerebral blood velocity (CBV) is affected by acute sport-related concussion. Design Prospective Cohort. Setting Laboratory. Participants: 136 male contact-sport athletes (19.1±1.4 years) recruited, subset of 14 sustained concussions (19±1.4 years). Intervention Participants completed baseline (T0) and post-injury testing at 72-hours (T1), 2-weeks (T2), and 1-month (T3). BP was monitored via finger photoplethysmography, and transcranial Doppler ultrasound indexed CBV in the middle cerebral artery. Squat-stand manoeuvers were performed at 0.05 and 0.10Hz to enhance BP variation. RM-ANOVA independent variables included time (4) and frequency (2). Outcome measures Transfer function analysis point estimates quantified coherence (correlation), phase (synchronisation) and gain (amplitude buffer) metrics between BP and CBV waveforms. Results Significant frequency-time interactions for phase (p=0.007) and gain (p=0.049). Simple effects analysis revealed time effects for phase at 0.10 Hz, indicating reductions at T1 (95% CI: 0.033 – 0.24 rads, p=0.008) and T2 (95% CI: 0.014–0.196 rads, p=0.02) compared to T0. On average, return-to-play occurred at T2 (median 14 days). Phase reductions at T1 were correlated with Standardised Assessment of Concussion performance (r=0.659, p=0.02). Conclusions These results reveal transient post-concussion impairments in the capacity of the cerebrovasculature to buffer BP oscillations, which exceeded clinical recovery duration. Phase reductions at 0.10Hz suggest the presence of a cerebrovascular autonomic dysregulation, which could leave the brain less protected to BP surges. This key finding may help explain why the brain is more vulnerable to additional trauma during the post-injury recovery period. Competing interests None.
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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) | 0.003 | 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".