11.32 Down three: does a reduction in on-field players influence head impacts in Canadian youth tackle football?
Bibliographic record
Abstract
Objective To compare head impact rates in a modified 9-on-9 Bantam (13–15 years old) Canadian football season to a traditional 12-on-12 season using video-analysis. Design Prospective cohort. Setting Football fields (Calgary, Canada). Participants In 2020, 384 youth football players (N=18 teams) and in 2021, 500 players (N=12 teams) participated. Video-analysis data was anonymized. Interventions (or Assessment of Risk Factors) Adhering to provincial COVID-19 cohort restrictions, the number of on-field players was reduced to 9-a-side in 2020, returning to 12-a-side in 2021. Independent variables included the number of on-field players, game type (i.e., regular season, playoffs), play type, player position, player role, impact location, and impact object (e.g., helmet, ground). Outcome Measures Head impacts (HI) were analyzed using Dartfish video-analysis software. HIs were stratified by team unit (e.g., offensive, defensive, kicking team, receiving team). Using negative binomial regression, HI rates (/100 player-plays and/10 gameplay-minutes) and incidence rate ratios (IRR) were estimated to examine differences between years. Main Results No differences were identified between 9-on-9 and 12-on-12 seasons for offense HI (IRRPlays=0.92, 95% CI; 0.75–1.12, IRRGameMins=0.90, 95% CI; 0.75–1.15) or any other team unit. The offensive team unit, however, experienced a significantly higher HI rate in the 12-on-12 format during playoffs versus the 12-on-12 regular season (IRRPlays=1.33, 95% CI; 1.07–1.65, IRRGameMins=1.26, 95% CI; 1.03–1.56). Conclusions No differences in HI were found between the 9-on-9 and 12-on-12 seasons for any team unit. Future research should consider field player-density and combining HI accelerometry, video-analysis, and injury surveillance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".