Influence of play type on the magnitude and number of head impacts sustained in youth American football
Bibliographic record
Abstract
The magnitude and number of head impacts experienced by young American football players are associated with negative brain health outcomes and may be affected by play-type strategies. The purpose of this research was to examine how play type affects the magnitude and number of head impacts in youth American tackle football. Head impacts were recorded for 30 games in the 5–9 age category and 30 games in the 9–14 age category. Impacts using physical and finite element models were conducted to determine the brain strain. Run plays had a higher head impact frequency in both age groups (p < 0.05). This increase in head impacts was consistent for all positions (p < 0.05), except wide receiver, and offensive line and defensive back in the 9–14 age group (p > 0.05). Both age groups experienced significantly different magnitude proportions with higher numbers of very low and low strain magnitude impacts during run plays (p < 0.05), and a higher proportion of moderate magnitude impacts in the 5–9 age category (p < 0.05). This data can be used to inform and educate teams and coaches and influence decisions around the use of runs and passing plays that may lead to a decrease in head impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".