A history of multiple concussions does not confer long-term impairments to dynamic cerebral autoregulation
Bibliographic record
Abstract
Objective To examine the effects of concussion history on an index of cerebral autoregulation in a healthy athlete population. Design Retrospective Cohort. Setting Laboratory. Participants 136 male contact sport athletes (19.1±1.4 years, 66 football, 70 hockey) were recruited; 39 presented with 0 previous concussions, 16 with 3+ previous concussions; exclusion criteria included history of concussion within 6 months. Intervention Participants completed baseline testing prior to the athletic season. Squat-stand manoeuvres at 0.05 and 0.10 Hz were used to enhance blood pressure (BP) variation. BP and cerebral blood velocity (CBV) in the middle cerebral artery were indexed non-invasively via finger photoplethysmography and transcranial Doppler ultrasound, respectively. Independent variables included sport (2) and concussion history (2). Outcomes Transfer function analysis point estimates at 0.05 and 0.10 Hz characterised the coherence (correlation), phase (synchronisation), and gain (amplitude buffer) between BP and CBV waveforms during the squat-stand manoeuvres. Results These findings suggest that, despite reductions in 0.10 Hz phase lead being reported acutely after concussion, multiple concussions do not appear to induce long-term impairments in dynamic cerebral autoregulation metrics. This is an important revelation as it indicates that the cerebrovasculature is able to adequately recover following the acutely diminished buffering capacity associated with a concussive injury Conclusions These findings suggest that, despite reductions in 0.10 Hz phase lead being reported acutely after concussion, multiple concussions do not appear to induce long-term impairments in dynamic cerebral autoregulation metrics. This is an important revelation as it indicates that the cerebrovasculature is able to adequately recover following the acutely diminished buffering capacity associated with a concussive injury 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".