MétaCan
Menu
Back to cohort
Record W7005965389

Subconcussive head impacts in sport: A systematic review of the evidence

2018· article· en· W7005965389 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAthletesPoison controlInjury preventionWeb of scienceHuman factors and ergonomicsHead injurySuicide preventionConcussion
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.062
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0180.018
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.019
GPT teacher head0.285
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicLepidoptera: Biology and TaxonomyFrench-language works237,207