Concussion in Professional Football: Comparison with Boxing Head Impacts—Part 10
Bibliographic record
Abstract
OBJECTIVE: This study addresses impact biomechanics from boxing punches causing translational and rotational head acceleration. Olympic boxers threw four different punches at an instrumented Hybrid III dummy and responses were compared with laboratory-reconstructed NFL concussions. METHODS: Eleven Olympic boxers weighing 51 to 130 kg (112-285 lb) delivered 78 blows to the head of the Hybrid III dummy, including hooks, uppercuts and straight punches to the forehead and jaw. Instrumentation included translational and rotational head acceleration and neck loads in the dummy. Biaxial acceleration was measured in the boxer's hand to determine punch force. High-speed video recorded each blow. Hybrid III head responses and finite element (FE) brain modeling were compared to similarly determined responses from reconstructed NFL concussions. RESULTS: The hook produced the highest change in hand velocity (11.0 +/- 3.4 m/s) and greatest punch force (4405 +/- 2318 N) with average neck load of 855 +/- 537 N. It caused head translational and rotational accelerations of 71.2 +/- 32.2 g and 9306 +/- 4485 r/s. These levels are consistent with those causing concussion in NFL impacts. However, the head injury criterion (HIC) for boxing punches was lower than for NFL concussions because of shorter duration acceleration. Boxers deliver punches with proportionately more rotational than translational acceleration than in football concussion. Boxing punches have a 65 mm effective radius from the head cg, which is almost double the 34 mm in football. A smaller radius in football prevents the helmets from sliding off each other in a tackle. CONCLUSION: Olympic boxers deliver punches with high impact velocity but lower HIC and translational acceleration than in football impacts because of a lower effective punch mass. They cause proportionately more rotational acceleration than in football. Modeling shows that the greatest strain is in the midbrain late in the exposure, after the primary impact acceleration in boxing and football.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".