Head Impact Magnitude in American High School Football
Bibliographic record
Abstract
OBJECTIVES: To describe determinants of head impact magnitudes between various play aspects in high school football. METHODS: Thirty-two high school American football players wore Head Impact Telemetry System instrumented helmets to capture head impact magnitude (linear acceleration, rotational acceleration, and Head Impact Technology severity profile [HITsp]). We captured and analyzed video from 13 games (n = 3888 viewable head impacts) to determine the following play aspects: quarter, impact cause, play type, closing distance, double head impact, player's stance, player's action, direction of gaze, athletic readiness, level of anticipation, player stationary, ball possession, receiving ball, and snapping ball. We conducted random intercepts general linear mixed models to assess the differences in head impact magnitude between play aspects (α = 0.05). RESULTS: The following aspects resulted in greater head impact magnitude: impacts during the second quarter (HITsp: P = .03); contact with another player (linear, rotational, HITsp: P < .001); initial head impact when the head is struck twice (linear, rotational, HITsp: P < .001); longer closing distances, especially when combined with a 3-point stance or when being struck in the head (linear: P = .03); the 2-point stance (linear, rotational, HITsp: P < .001); and offensive linemen not snapping the ball compared with those snapping the ball (rotational: P = .02, HITsp: P = .02). CONCLUSIONS: Preventing head impacts caused by contact with another player may reduce head impact magnitude in high school football. Rule or coaching changes that reduce collisions after long closing distances, especially when combined with the 3-point stance or when a player is being struck in the head, should be considered.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".