A Review of Impact Testing Methods for Headgear in Sports: Considerations for Improved Prevention of Head Injury Through Research and Standards
Bibliographic record
Abstract
Standards for sports headgear were introduced as far back as the 1960s and many have remained substantially unchanged to present day. Since this time, headgear has virtually eliminated catastrophic head injuries such as skull fractures and changed the landscape of head injuries in sports. Mild traumatic brain injury (mTBI) is now a prevalent concern and the effectiveness of headgear in mitigating mTBI is inconclusive for most sports. Given that most current headgear standards are confined to attenuating linear head mechanics and recent brain injury studies have underscored the importance of angular mechanics in the genesis of mTBI, new or expanded standards are needed to foster headgear development and assess headgear performance that addresses all types of sport-related head and brain injuries. The aim of this review is to provide a basis for developing new sports headgear impact tests for standards by summarizing and critiquing: 1) impact testing procedures currently codified in published headgear standards for sports and 2) new or proposed headgear impact test procedures in published literature and/or relevant conferences. Research areas identified as needing further knowledge to support standards test development include defining sports-specific head impact conditions, establishing injury and age appropriate headgear assessment criteria, and the development of headgear specific head and neck surrogates for at-risk populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".