Player position in American football influences the magnitude of mechanical strains produced in the location of chronic traumatic encephalopathy pathology: A computational modelling study
Bibliographic record
Abstract
American football players are frequently exposed to head impacts, which can cause concussions and may lead to neurodegenerative diseases such as chronic traumatic encephalopathy (CTE). Player position appears to influence the risk of concussion but there is limited work on its effect on the risk of CTE. Computational modelling has shown that large brain deformations during head impacts co-localise with CTE pathology in sulci. Here we test whether player position has an effect on brain deformation within the sulci, a possible biomechanical trigger for CTE. We physically reconstructed 148 head impact events from video footage of American Football games. Players were separated into 3 different position profiles based on the magnitude and frequency of impacts. A detailed finite element model of TBI was then used to predict Green-Lagrange strain and strain rate across the brain and in sulci. Using a one-way ANOVA, we found that in positions where players were exposed to large magnitude and low frequency impacts (e.g. defensive back and wide receiver), strain and strain rate across the brain and in sulci were highest. We also found that rotational head motion is a key determinant in producing large strains and strain rates in the sulci. Our results suggest that player position has a significant effect on impact kinematics, influencing the magnitude of deformations within sulci, which spatially corresponds to where CTE pathology is observed. This work can inform future studies investigating different player-position risks for concussion and CTE and guide design of prevention systems.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".