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Record W1991639352 · doi:10.1080/17457300.2012.749918

Mountain bike terrain park-related injuries: an emerging cause of morbidity

2012· article· en· W1991639352 on OpenAlexafffund
Nicole Romanow, Brent Hagel, Michelle Nguyen, Tania Embree, Brian H. Rowe

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsProvincial Laboratory of Public HealthUniversity of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchChildren's Hospital FoundationGovernment of CanadaFondation pour la Recherche MédicaleAlberta Children's Hospital FoundationHealth Research Board
KeywordsMedicineFoothillsInjury preventionPoison controlOccupational safety and healthSuicide preventionHuman factors and ergonomicsMedical emergencyEmergency departmentEmergency medicinePhysical therapyGeographyNursing

Abstract

fetched live from OpenAlex

This case-control study describes the profile of bicyclists injured in mountain bike terrain parks (MBTPs) and examines risk factors for severe injury among MBTP riders. Cases were hospitalised bicyclists injured in MBTPs. Controls were bicyclists injured in MBTPs who were discharged from the emergency department. No significant differences were observed in the distribution of age and sex between cases and controls. A higher proportion of cases compared with controls suffered a head injury (22%), fracture (41%) or internal organ injury (32%). Controls suffered a higher proportion of superficial injuries (26%), sprains (10%) or wounds (16%). Upper extremity protective equipment (e.g. elbow or shoulder pads) was used more by cases than controls (23% vs. 11%, p = 0.03). Riders who self-reported cycling faster than usual had significantly higher risk of severe injury compared with others. The risk of severe injury may be reduced by encouraging bicyclists to control their speed or by modifying MBTP design to limit the opportunity to gain speed.

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)0.003
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 score0.278
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.346
Teacher spread0.326 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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