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Record W1587516313

Snowmobile trauma: 10 years' experience at Manitoba's tertiary trauma centre.

2004· article· en· W1587516313 on OpenAlexaffabout
Rena L. Stewart, G. Brian Black

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineInjury preventionPoison controlOccupational safety and healthHuman factors and ergonomicsRecreationSuicide preventionEnvironmental healthAlcohol consumptionPopularityDemographyAlcoholPathologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: According to the literature, the increased recreational use of the snowmobile has resulted in an increasing number of musculoskeletal injuries. We wished to examine whether previously described risk factors continue to be associated with snowmobile trauma and to identify previously unrecognized risks and specific patterns of injury. METHODS: We carried out a chart review of all snowmobile-related injuries over a 10-year period at the Health Sciences Centre in Winnipeg, the only level 1 trauma centre serving the Province of Manitoba, with particular attention to the risk factors of suboptimal lighting, excessive speed and alcohol consumption. RESULTS: We identified 480 injuries in 294 patients, and 81 (27.6%) of these patients died. Collisions accounted for 72% of the injury mechanisms. Of the injuries sustained, 31% occurred on roads. Excessive speed was a risk factor in 54% of patients, suboptimal lighting in 86% and a blood alcohol level greater than 0.08 in 70%. Musculoskeletal injuries accounted for 57% of those recorded. There were also brachial plexus injuries (3%) and knee dislocations (2%). To our knowledge, this is the largest study detailing injury associated with recreational use of snowmobiles in Canada. CONCLUSIONS: Because snowmobile trauma is caused principally by human errors, it is potentially preventable. Efforts aimed at prevention must focus on the driver, who controls the common risk factors. The danger of snowmobiling while intoxicated must be emphasized. Trail-side monitoring is likely to be ineffective, as the majority of accidents do not occur on designated snowmobile trails.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.183
Teacher spread0.167 · 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 designOther design
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

Citations21
Published2004
Admission routes2
Has abstractyes

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