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WHAT ARE THE RISK FACTORS FOR INJURY TO SKIERS AND SNOWBOARDERS IN TERRAIN PARKS AND WHICH STRATEGIES ARE EFFECTIVE IN REDUCING THE RISK OF INJURY? A SYSTEMATIC REVIEW

2017· review· en· W2741974989 on OpenAlexaff
Olivier Audet, Tatum Priyambada Mitra, Carolyn A. Emery, Brent Hagel, Alison Macpherson, Claude Goulet

Bibliographic record

VenueBritish Journal of Sports Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsYork UniversityAlberta Children's HospitalUniversity of CalgaryUniversité Laval
Fundersnot available
KeywordsRecreationTerrainInjury preventionPhysical therapyPopulationPoison controlMedicineInclusion and exclusion criteriaHuman factors and ergonomicsPhysical medicine and rehabilitationMedical emergencyGeographyEnvironmental healthCartographyAlternative medicinePathology

Abstract

fetched live from OpenAlex

Background The prevalence of terrain parks (TPs) in ski areas has grown over the past two decades. TPs are specific areas of the slopes where terrain is modified to accommodate acrobatic maneuvers. There is evidence that TP injuries are more severe than injuries sustained on regular slopes. However, injury prevention strategies in TPs still need to be explored. Objective To identify the risk factors for skiing and snowboarding injuries in TPs and assess the effectiveness of injury prevention strategies. Participants/population Recreational skiing or snowboarding injuries in a TP. Methods Searches in electronic literature databases and hand-searches in Skiing Trauma volumes and bibliographies were performed. A search strategy involving a combination of Subject Headings and keywords and related synonyms using OR, with the Boolean operator AND was performed. Reviewers independently performed an initial screen of all titles and published abstracts for inclusion criteria to determine full text required. Criteria were (1) original data; (2) injury sustained in terrain parks/half-pipes; (3) recreational ski or snowboard injury; (4) study design with comparison group. Authors reviewed included papers and extracted data into structured tables. Results The search strategy identified 62 relevant studies. Only 11 studies met inclusion criteria including one intervention study. Results suggest that removing man-made jumps reduces the risk of severe injury. According to preliminary results, type of slope, age, gender, and skill level are common risk factors. Critical appraisal of articles is in process. An overview suggests that risk factors studies in this area (n=10) are heterogeneous. With primarily cross-sectional and case-control study designs, the risk of associated bias is significant. Conclusions One intervention study suggests that removing man-made jumps from TPs reduces the risk of severe injuries. Further research is needed to better understand injury prevention strategies that are effective in reducing the risk of injury in TPs.

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)0.012
metaresearch head score (Gemma)0.068
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.068
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0120.010
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.018
GPT teacher head0.337
Teacher spread0.318 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations2
Published2017
Admission routes1
Has abstractyes

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