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Record W2283405686 · doi:10.1097/jsm.0000000000000235

Combative Sports Injuries

2015· article· en· W2283405686 on OpenAlexafffundabout
Shelby Karpman, Patrick Reid, Leah Phillips, Ziling Qin, Douglas P. Gross

Bibliographic record

VenueClinical Journal of Sport Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of AlbertaAlberta Health Services
FundersUniversity of Alberta
KeywordsMedicineConcussionIncidence (geometry)Physical therapyInjury preventionPoison controlMartial artsOccupational safety and healthEmergency medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Mixed martial arts (MMA) is an increasingly popular combative sport involving aggressive techniques that present substantial injury risk. We examined the incidence and types of injuries sustained in MMA fights and compared this with injuries sustained in boxing matches. DESIGN: Consecutive Case Series. SETTING: We used data from post-fight medical examinations on all bouts in Edmonton, Canada, between 2000 and 2013. PARTICIPANTS: The participants were 1181 MMA competitors and 550 boxers. MAIN OUTCOME MEASURES: The attending physician conducted a mandatory post-fight examination of all fighters and documented the nature of injuries sustained. RESULTS: Boxers were significantly more likely not to experience injury (49.8% vs 59.4%, P < 0.001), whereas MMA fighters were significantly more likely to experience 1 injury (typically contusion/bruising, P < 0.001). Boxers were more likely to experience loss of consciousness (7.1% vs 4.2%, P = 0.01) and serious eye injury (1.1% vs 0.3%, P = 0.02). CONCLUSIONS: The overall injury incidence in MMA competitors appears slightly higher than for boxers, but MMA fighters experience more minor contusion/bruising injuries. Boxers are more likely to experience serious injury such as concussion/head trauma involving loss of consciousness or eye injury such as retinal detachment.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.235
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.446
Teacher spread0.346 · 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 teacher head, 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

Citations54
Published2015
Admission routes3
Has abstractyes

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