Age, power, and sport-related concussion. Is there a tension between sport performance and concussion risk in tackle football?
Bibliographic record
Abstract
Sport-related concussion (SRC) poses a barrier to otherwise health-promoting sport participation. Tackle Football (American Football) is a popular sport with an elevated risk of SRC, which prioritises strength and conditioning. The association between physical performance, notably lower body maximal muscle power and concussion rate, is not well understood in youth tackle football. This prospective cohort study investigated the association between lower body power and concussion rate in 595 Canadian adolescent-age (ages 14-18) tackle football players. Data collection included baseline characteristics (height, weight, vertical jump, age, player position, concussion history), sessional practice and game player attendance, and validated injury surveillance to identify SRC incidence. Missing baseline data were estimated using multivariable imputation by chained equations. Multilevel multivariable Poisson Regression analyses were used to estimate incidence rate ratios (IRR) examining the association between lower body power and SRC rates (adjusted for position, age, concussion history), clustered by team, and offset by player session participation time (hours). Higher lower body power was associated with a 3.18-fold higher concussion rate per 1000 W from 2000W to 5000 W (IRR = 3.18, 95% CI; 1.24-8.17). Age was associated with a 0.49-fold lower SRC rate (IRR = 0.49, 95% CI; 1.08-4.00). Participants with higher lower body power and younger age have higher SRC rates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".