A Descriptive Video Analysis of Helmet Impact Cases in North American Youth Football Players
Bibliographic record
Abstract
Background: Detailed characterization of on-field helmet impacts in football through video analysis has mostly been limited to professional games due to the availability of high quality, multi-view video (e.g., broadcast footage). Few studies have assessed youth football helmet impacts using video-based methods, often with only a single-camera view. Objective: A multi-camera approach was used in this observation-based study to describe the mechanisms and situational factors of in-game helmet impacts experienced by youth football players. Methods: A descriptive video analysis was performed in which video of three games from two old divisions (game A: 9–12 years; games B and C: 13–14 years) was reviewed and parameters related to all cases of observed helmet impact were documented. Results: Overall, 95 helmet impact cases were identified (single helmet contact: 81.1%; multiple helmet contacts: 18.9%), with 115 helmet contacts. Helmet-to-ground contacts were most common (59.1%), followed by helmet-to-helmet (24.3%) and helmet-to-body (16.5%). Helmet impact cases generally occurred during a rush play (67.4%) and were concentrated in the mid-field (81%). Helmet contact locations were predominantly distributed between the rear (upper) (28.7%) and side (upper) (27.8%) helmet regions. Tackling was the most frequent activity leading to helmet impact (41.1%). Conclusion: These findings offer detailed on-field helmet impact characteristics at the youth level that can help inform athlete safety improvement efforts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".