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Record W2153939688 · doi:10.1136/ip.6.1.59

Equestrian injuries: a five year review of hospital admissions in British Columbia, Canada

2000· article· en· W2153939688 on OpenAlexaffabout
Janet M Sorli

Bibliographic record

VenueInjury Prevention · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsOccupational safety and healthPoison controlInjury preventionSuicide preventionForensic engineeringMedical emergencyHuman factors and ergonomicsMedicineEmergency medicineEngineering

Abstract

fetched live from OpenAlex

AIM: To determine the demographics of hospital admissions and mortality associated with equestrian activities in the 33,000 riders in British Columbia (BC). METHOD: Analysis of admission data from the Ministry of Health for the years 1991-96, review of information obtained from the Office of the Chief Coroner, and comparison of data from Canadian Hospitals Injury Reporting and Prevention Program. RESULTS: The mean number of admissions per year was 390. Head injury was the most common cause of admission to hospital (20%) in BC. Females most often required admission (62%). Teenagers and children have a higher incidence of head injuries than the general population. The injury rate was 0.49/1000 hours of riding. There were three deaths per year, 1/10,000 riders; 60% were caused by head injury and females predominated. CONCLUSION: Head injuries and other serious injuries occur with equestrian activities and it is important for doctors, instructors, and parents to promote the use of appropriate safety equipment, including helmets, especially for children.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.039
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0070.018
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.215
Teacher spread0.209 · 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

Citations117
Published2000
Admission routes2
Has abstractyes

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