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Record W2014794065 · doi:10.1097/jsm.0b013e318218acdd

Rodeo Catastrophic Injuries and Registry: Initial Retrospective and Prospective Report

2011· article· en· W2014794065 on OpenAlexaffabout
Dale J. Butterwick, Mark R. Lafave, Breda H. F. Lau, Tandy R Freeman

Bibliographic record

VenueClinical Journal of Sport Medicine · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsMount Royal UniversityUniversity of Calgary
FundersHealth Research Board
KeywordsMedicineRetrospective cohort studyMedical emergencyEmergency medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: To introduce the Rodeo Catastrophic Injury Registry (RCIR) and quantify the nature and incidence of catastrophic injury and fatality in rodeo participants across North America. DESIGN: Retrospective and prospective collection of catastrophic and fatal injury data in rodeo using an online registry (RCIR). SETTING: Canada and the United States. PARTICIPANTS: North American rodeo competitors. ASSESSMENT OF RISK FACTORS: Age, gender, level of competition, rodeo event, mechanism of injury, and use of protective equipment. MAIN OUTCOME MEASURES: Frequency, incidence, and nature of catastrophic injuries and fatalities among rodeo participants. RESULTS: The incidence rate of catastrophic injury from 1989 to 2009 was 9.45 per 100 000 (49/518 286). The incidence rate of catastrophic injury during the 2007-2009 study period was 19.81 per 100 000 (19/95 892). The incidence rate of fatality from 1989 to 2009 was 4.05 per 100 000 (21/518 286). The incidence rate of fatality for the 2007-2009 study period was 7.29 per 100 000 (7/95 892). CONCLUSIONS: Thoracic compression mechanisms of injury are most pervasive and likely to be fatal in rodeo and bull riding. It is unknown whether rodeo protective vests have a protective effect in reducing catastrophic and fatal injuries. On the contrary, helmet use in bull riding and rodeo events seems to have a protective effect in reducing both catastrophic injury and fatality.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.009
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.306
Teacher spread0.264 · 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

Citations19
Published2011
Admission routes2
Has abstractyes

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