Long-term effects of multiple concussions on prefrontal cortex oxygenation during repeated squat-stands in retired contact sport athletes
Bibliographic record
Abstract
Background This study investigated the long-term effects of multiple concussions on prefrontal cortex oxygenation using near-infrared spectroscopy (NIRS) during a squat-stand maneuver that activated dynamic cerebral autoregulation.Methods Active male retired contact sport athletes with a history of 3+ concussions (mTBI; n = 55), and active retired athletes with no concussion history (CTRL; n = 29) were recruited. Participants completed a 5-min squat-stand maneuve (10-s squat, 10-s stand, 0.05 Hz; 15 times). Oxygenated (O2Hb), deoxygenated (HHb), total (tHb) hemoglobin, and hemoglobin difference (HbDiff) were analyzed through the change in maximal and minimal values during the test (∆MAX), Z-scores, and standard deviations.Results mTBI group showed left prefrontal cortex O2Hb ∆MAX (p = 0.046) and HbDiff ∆MAX (p = 0.018) were significantly higher. Within-group analyses showed significantly higher left HHb ∆MAX (p = 0.003) and lower left HbDiff Z-scores (p = 0.010) only in the mTBI group. The CTRL group demonstrated significantly lower left HbDiff SD (p = 0.039), tHb Z-scores (p = 0.030), and HbDiff ∆MAX (p = 0.037) compared to right prefrontal cortex response.Conclusion These preliminary results suggest changes in prefrontal cortex oxygenation potentially affecting the brain’s ability to adapt to changing cerebral perfusion pressure after multiple previous concussions.
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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 | 0.000 |
| 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) | 0.001 | 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".