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Record W1854546266 · doi:10.1249/jsr.0b013e3181df9211

Injury Prevention Strategies in Skiers and Snowboarders

2010· article· en· W1854546266 on OpenAlexaboutno aff
Donald Hansom, Alasdair Sutherland

Bibliographic record

VenueCurrent Sports Medicine Reports · 2010
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAthletesInjury preventionMedicineInjury surveillancePhysical therapyAnterior cruciate ligamentPoison controlOccupational safety and healthSuicide preventionSports medicineForensic engineeringMedical emergencyEngineeringSurgery

Abstract

fetched live from OpenAlex

With associated snow sports injury risks being well documented, the scope for preventative medicine is clear. This article focuses on potential precautions taken by skiers/snowboarders and how they vary between different snow sport groups. A total of 181 randomly selected individuals from the Whistler/Blackcomb Medical Centre, Canada, took part in this questionnaire-based cohort study, examining snow sports activity, preventive measures, and protective equipment use. Most individuals were ski area residents of advanced expertise, with 50% having had a previous snow-riding injury. The most commonly reported significant ski injury was anterior cruciate ligament (ACL) rupture, while in snowboarders, it was wrist fracture. Skiers were more likely to improve fitness levels before going onto the slopes, whereas crossover riders (both skiing and snowboarding) were more likely to take lessons. Most individuals used eye and sun protection and had adequately warm clothing. Beyond basic precautions, protective equipment and preventive measures are used infrequently. It is suggested that by increasing awareness and use of such equipment, injury rates may be reduced.

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 score0.213
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.333
Teacher spread0.321 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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