Bibliographic record
Abstract
Swartz EE, Register-Mihalik JK, Broglio SP, et al. National Athletic Trainers' Association Position Statement: Reducing Intentional Head-First Contact Behavior in American Football Players. J Athl Train. 2022;57(2):113–124. doi: https://doi.org/10.4085/1062-6050-0062.21Please see the authors' disclosures below.Erik E. Swartz, PhD, ATC, has received grants from the GOG Hawaii Foundation and the Nine Sigma Head Health Challenge and trademark registration for HUTT. Johna K. Register-Mihalik, PhD, LAT, ATC, has received grants from the National Operating Committee on Standards for Athletic Equipment, Department of Defense (DOD), Centers for Disease Control and Prevention (CDC), National Center for Injury Prevention and Control, National Athletic Trainers' Association (NATA) Foundation, National Football League (NFL), and National Collegiate Athletic Association (NCAA)–DOD Mind Matters Research Challenge Award; she is a member of USA Football's Football Development Council. Steven P. Broglio, PhD, ATC, has received funding from the National Institutes of Health, CDC, DOD-USA Medical Research Acquisition Activity, NCAA, NATA Foundation, NFL, Under Armour/GE, Simbex, and ElmindA. He has consulted for the NCAA (travel expenses only), US Soccer, US Cycling (unpaid), and medicolegal litigation and received speaker honoraria and travel reimbursements for talks given. He is on the University of Calgary SHRed Concussions external advisory board (unpaid). Jason P. Mihalik, PhD, CAT(C), ATC, has received grants from the National Institutes of Health, NFL, CDC, and DOD. Kevin M. Guskiewicz, PhD, ATC, has received grants from the NFL (subaward from Boston Children's Hospital) and NCAA and nonfinancial support from the NCAA Scientific Committee. Julian Bailes, MD, is Chairman of the Medical Advisory Committee for Pop Warner Football. Jay L. Myers, PhD, reported no disclosures. Merrill Hoge, BA, has received royalties from Mascot Books for a football-related publication.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.879 | 0.817 |
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".