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Incidence Study of Head Blows and Concussions in Competition Taekwondo

2004· article· en· W2016528674 on OpenAlexaff
Jae-Ok Koh, J. David Cassidy

Bibliographic record

VenueClinical Journal of Sport Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsConcussionMedicineIncidence (geometry)Physical therapyHead injuryPoison controlInjury preventionPhysical medicine and rehabilitationSurgeryMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the incidence of head blows and concussions in competition taekwondo. DESIGN: Incidence cohort design. SETTING: Taekwondo tournament in 2001, in South Korea. PARTICIPANTS: A total of 2328 competitors (female, 676; male, 1652; age, 11-19 years) from 424 schools participated in the tournament. All recipients of head blows were interviewed immediately after the match. All matches were recorded on videotape. MAIN OUTCOME MEASURES: Head blow and concussion rates were calculated. Also, factors associated with head blows and concussions were analyzed. RESULTS: The incidence of head blows and concussions was 226 and 50 per 1000 athlete exposures, respectively. Only 17% of competitors reported that they had had a concussion in the last 12 months. A multinomial logistic model showed that head blows and concussions were associated with young age and a lack of blocking skills. CONCLUSIONS: The incidence of head blows and concussions is high in competition taekwondo. Promoting blocking skills to prevent head blows could decrease concussions in taekwondo.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.181
GPT teacher head0.498
Teacher spread0.317 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations60
Published2004
Admission routes1
Has abstractyes

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