Subconcussive head impacts in sport: A systematic review of the evidence
Bibliographic record
Abstract
Repetitive hits to the head that do not result in concussions are of increased concern to sport science and the sporting community. The term 'subconcussion' is widely used, not well understood, and requires further clarity. Therefore, the purpose of the study was to systematically review the literature and address two objectives: 1) To determine how 'subconcussion' is characterized in the current literature and 2) to evaluate the evidence on subconcussive impacts in sport. CINAHL, EMBASE, MedLine, PsycINFO, SportDiscus, and Web of Science were searched for articles that sought to assess subconcussive impacts or outcomes related to non-concussive head impact exposure in sport. A total of 1966 articles were screened, with 56 meeting the inclusion criteria and assessed for quality. The studies varied in focus from neurobiology, neuropsychology, to impact exposures. The main finding of the review was that repetitive head impacts in male athletes was associated with functional and microstructural deterioration. Whether these changes represent injury is unclear. Conclusions about female athletes could not be drawn because they were underrepresented in all categories of study. The term 'subconcussion' itself was inconsistently used across the literature, and was poorly defined. 'Subconcussive impacts', 'head impacts', or 'repetitive hits to the head', without inference to injury, may be more appropriate and less confusing. Future research is needed to investigate the phenomenon more thoroughly, and to advance our understanding of how exposure to head impacts affects the brains of athletes in the short and long-term.Acknowledgments: We would like to acknowledge Julian Clarke
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".