11.28 Who’s keeping score? The effect of a mercy rule on head impact rates in Canadian high-school tackle football games
Bibliographic record
Abstract
Objective To examine the association between the Mercy Rule (MR) and head impact incidence rates (IR) in Canadian high school football games. Design Cross-sectional video-analysis study. Setting Football fields (Calgary, Canada). Participants In 2019, two high-school football teams (ages 15–16) in Calgary, Canada had a total of 16 team-games (N=8 games) videorecorded and analyzed using Dartfish software. Interventions (or Assessment of Risk Factors) As per Football Canada’s Tackle Football Rulebook, the MR (initiates continuous running time) comes into effect when the score differential becomes 35 points or more in the second half of the game. Outcome Measures The outcome measure was head impacts. A negative binomial regression adjusted for cluster by team-game was used to estimate head impact IRs (/team-game and/team-2nd-half) for games with and without the MR in effect. The corresponding incidence rate ratios (IRR) were also estimated. Main Results Games with the MR in effect had 28% fewer plays (39% fewer in the 2nd-half) and 27% fewer head impacts per team-game [IR=241.67 (95%CI:199.24, 293.13)] compared with games without [IR=328.91 (95%CI:313.04, 345.57); IRR=0.73 (95% CI:0.61, 0.89)]. In the 2nd-half of games with the MR in effect there were 40% fewer head impacts per team-2nd-half [IR2nd-half=99.16 (95%CI:76.81, 128.03)] compared with games without [IR2nd-half=166.20 (95%CI:156.75, 176.22); IRR2nd-half=0.60 (95%CI:0.47, 0.76]. Conclusions In this novel evaluation of the Mercy Rule, running time was associated with a 27% lower head impact IR in games (40% in 2nd-half). Consideration for future research should include examining the effect of the MR on concussion IRs.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".