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Record W2800214236 · doi:10.1080/15438627.2018.1438277

Pediatric and adolescent injury in snowboarding

2018· review· en· W2800214236 on OpenAlexafffund
Kelly Russell, Erin Selci

Bibliographic record

VenueResearch in Sports Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsManitoba Harm Reduction NetworkUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersResearch ManitobaManitoba Health Research Council
KeywordsMedicineInjury preventionOccupational safety and healthInjury surveillancePhysical therapyPoison controlHuman factors and ergonomicsHead injurySuicide preventionMusculoskeletal injuryMedical emergencySurgeryAlternative medicine

Abstract

fetched live from OpenAlex

To systematically review published literature on pediatric snowboard injuries, a literature search was performed in PubMed for "snowboard*". Studies must 1) have been primary research; 2) included at least 10 snowboarders; 3) included children and/or adolescents 4) reported specific injury outcomes, risk factors, or injury prevention program effectiveness. The overall injury rates ranged from 0.5 per 1,000 runs to 420 per 1,000 snowboarders. The most common injuries types were fractures, sprains and strains. Most injuries occurred to an upper extremity or the head. Falls and collisions were the most common mechanisms. Snowboarders who were younger, female, had less snowboard experience, or had a previous injury were at greater risk for injury. Wearing wrist guards had a protective effect. Injury rates varied by injury denominator and source of data. Injury prevention efforts should evaluate modifiable extrinsic risk factors, such as strategies to increase use of protective equipment.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0120.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.151
GPT teacher head0.482
Teacher spread0.331 · 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 designNot applicable
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

Citations11
Published2018
Admission routes2
Has abstractyes

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