Cerebral Haemodynamic Assessment Following Sport-related Concussion (Mild Traumatic Brain Injury) in Youth and Amateur Rugby Union Players
Bibliographic record
Abstract
Abstract Background Using functional near-infrared spectroscopy (fNIRS) as an objective diagnostic tool, we aimed to (1) compare fNIRS measurements in adult and youth male rugby players against controls over a playing season, and 2) document the fNIRS changes that occur in concussed rugby players during the England Rugby Football Union Graduated Return-To-Play programme (GRTP). Sixty-seven participants (rugby = 41 (26 adults: 27.5 ± 4.4 years; 15 youth: 16.5 ± 0.6 years; control = 26 (11 adult: 30.5 ± 5.2 years; 15 youth: 16.9 ± 0.4 years) completed fNIRS assessments at pre, mid and end-season. Eight players (five youth, three adult) sustained concussions, and completed fNIRS and the Graded Symptom Checklist from the Sport Concussion Assessment Tool version 5 (SCAT5) assessment throughout the GRTP period. Mixed linear models were utilised to assess the effect of group and time on fNIRS measures of oxyhaemoglobin (∆O2Hb) and deoxyhaemoglobin (∆HHb) during performance tasks. Typical Error (TE) i.e., normal biological fluctuation and measurement error, was calculated to identify ‘cut-off’ thresholds for identifying effects of concussion. Results There were significant differences in fNIRS indices over time in adult and youth groups (p < 0.05) but no significant differences between rugby and control groups (p > 0.05). Seven out of eight (87.5%) concussed players showed changes greater than TE during the GRTP period for both ∆O2Hb and ∆HHb during performance tasks and these players’ ∆O2Hb profiles had not returned to within ‘normal’ levels within the GRTP period. All players’ symptom severity and number returned to normal within the GRTP period. Conclusion Current GRTP protocols alone are problematic and there is a need for a more individualised approach to concussion management, utilising objective biomarker tools such as fNIRS.
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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 | 0.000 | 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".