7.11 Blood flow differences between sexes in athletes with history of concussion
Bibliographic record
Abstract
Objective To detect differences in cervical arteries that contribute to cerebral blood flow (Common Carotid Artery; CCA, Internal Carotid Artery; ICA and Vertebral artery; VA) between athletes with a concussion history (Hx) and athletes without concussion history (no Hx). We hypothesized that athletes with Hx would have reduced blood flow volume compared to athletes with no Hx. Design Prospective study. Setting Canadian University. Participants Eighty-two asymptomatic university athletes during pre-season baseline testing from 6 teams (42 females, 40 males); 36 athletes (18 females, 18 males) reported having Hx and 46 athletes (24 females, 22 males) had none. Interventions (or Assessment of Risk Factors) Doppler ultrasound measured blood flow volume in CCA, ICA, and VA bilaterally. Outcome Measures Blood flow volume differences in 6 cervical arteries that contribute to cerebral blood flow. Main Results Females with no Hx had significantly lower blood flow volume (BFV) compared to males with no Hx in right CCA (β=-83.78; p=0.009); left CCA (β=-72.40; p=0.027); and right ICA (β=-74.84 ; p=0.010). Females with Hx had significantly lower BFV in right CCA (β=-140.79; p <0.001); left CCA (β=-126.895; p <0.001); and right ICA (β=-101.47; p=0.001). All beta-coefficients were adjusted. There were no significant differences between males and females, regardless of Hx in the vertebral arteries bilaterally. Conclusions This preliminary data demonstrates sex-related differences, particularly in the anterior cervical vessels contributing to cerebral blood flow in athletes with Hx. This suggests soft tissue in the neck may be a mechanism influencing blood flow to the brain post-concussion injury.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".