7.12 How do multifaceted outcomes change following concussion and over recovery in youth schoolboy rugby players?
Bibliographic record
Abstract
Objective To evaluate acute changes in measures of (1) Symptom Severity Score (2) Cervical spine strength (3) and Buffalo Concussion Treadmill Test (BCTT) following concussion and throughout recovery. Design Prospective cohort study Setting Primary Care Centre, Dublin, Ireland Participants 135 male Senior Cup schoolboy rugby union players [16.7(±0.8)] from 5 schools. Interventions (or Assessment of Risk Factors) N/A Outcome Measures Participants completed the Sport Concussion Assessment Tool (SCAT3), cervical spine maximal strength testing (Newtons/kilogram) and BCTT at pre-season, acutely following concussion and weekly to the time of medical clearance to return to rugby. Main Results Of the 135 participants included in this study, 16 were diagnosed with a sport-related concussion (defined as per the consensus on concussion in sport) [Incidence rate= 11.85/100 players/season (95% CI; 6.9, 18.5)]. SCAT3 symptom severity scores increased from baseline to week one post-concussion by a median of 11.00 (95%CI; 7.0,30.5)(z=3.184,p=.001, n=14). Post injury to symptom burden scores decreased a median of 16.0 (95%CI;-46.0, -6.0)(z=2.81,p= <.005, n=11) points. The median change in composite neck strength score (n/kg) from baseline to post-concussion was -1.08n/kg (95%CI;-1.89, -0.54)(z=2.79, p=.005, n=14); and post-concussion to recovery increased a median of 1.79n/kg, 95%CI (-0.13,2.27)(z=2.31, p=.021, n=9). BCTT MAX HR from baseline to post-concussion decreased a median of 40.0bpm (95%CI; -72.0, -9.0)(z=-3.08, p=.002, n=14) and from post-concussion to recovery increased a median of 40.0bmp (95%CI; 23.0,91)(z+2.52, p=.012, n=8). Conclusions Symptom burden, cervical spine strength and BCTT outcomes all worsened following concussion and improved from initial post injury scores to the time of clearance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".