Individualized monitoring of longitudinal heading exposure in soccer
Bibliographic record
Abstract
There is growing concern that repetitive soccer headers may have negative long-term consequences on brain health. However, inconsistent and low-quality heading exposure measurements limit past investigations of this effect. Here we conducted a comprehensive heading exposure analysis across all players on a university women's soccer team for over two calendar years (36 unique athletes), quantifying both game and practice exposure during all in-season and off-season periods, with over ten thousand video-confirmed headers. Despite an average of approximately 2 headers per day, players' daily exposures ranged from 0 to 45 headers, accumulating to highly variable total exposure of 2-223 headers over each in-season/off-season period. Overall, practices and off-season periods accounted for 70% and 45% of headers, respectively. Impact sensor data showed that heading kinematics fell within a tight distribution, but sensors could not capture full heading exposure due to factors such as compliance. With first-of-its-kind complete heading exposure data, we recommend exposure assessments be made on an individual level and include practice/off-season collection in addition to games and competitive seasons. Commonly used group statistics do not capture highly variable exposures, and individualized complete heading exposure tracking needs to be incorporated in future study designs for confirming the potential brain injury risk associated with soccer heading.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".