Who Is 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 (IRs) in Canadian high school football games. DESIGN: Cross-sectional. SETTING: Calgary, Canada. PARTICIPANTS: Two high school football teams (ages 15-16 years) had a total of 16 team-games videorecorded and analyzed. INTERVENTIONS: The MR mandates continuous running time in the second half of games when the score differential is 35 points or greater. MAIN OUTCOME MEASURES: Head impact IRs and incidence rate ratios (IRRs) were used to compare head impact rates in MR and non-MR games. RESULTS: Mercy Rule games had 28% fewer plays, and the head impact IR per team-game was 27% lower [IRR, 0.73; 95% confidence interval (CI), 0.61-0.89] in MR games (IR, 241.67; 95% CI, 199.24, 293.13) than in non-MR games (IR, 328.91; 95% CI, 313.04, 345.57). Across all games (MR and non-MR), running plays accounted for more than half of all head impacts, and the head impact rates for running plays exceeded all other play types except for a sack of the quarterback. Players engaged in blocks (blocking or being blocked) accounted for 68.90% of all recorded head impacts. The highest proportion of impacts involved the front of the helmet (54.85%). There was no difference in head impact rates by player-play comparing MR and non-MR games (IRR, 1.01; 95% CI, 0.85, 1.19). CONCLUSIONS: Given the concerns for potential long-term consequences of repetitive head impacts, the MR is a prevention strategy by which head impact rates can be significantly lowered when a significant score differential exists.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".