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Record W4210953019 · doi:10.1055/s-0041-1731703

Musculoskeletal Injuries in Cross-Country Skiing

2022· article· en· W4210953019 on OpenAlexaboutno aff
Diego F. Lemos, Matthew G. Geeslin, Christopher Kanner, Mark E. Lach, Roar R. Pedersen

Bibliographic record

VenueSeminars in Musculoskeletal Radiology · 2022
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCross countryMicrotraumaMedicineRecreationPopularityModalitiesInjury preventionAlpine skiingSuicide preventionPoison controlPhysical therapyMedical emergencyPhysical medicine and rehabilitationDemographic economics

Abstract

fetched live from OpenAlex

Cross-country skiing, one of the oldest forms of skiing, is enjoyed widely as a recreational activity and as a competitive sport. It is practiced in regions with snow-covered landscapes, particularly in the Nordic countries and with increasing popularity in non-Nordic countries of Europe as well as in the United States, Canada, Australia, and New Zealand, among others. Cross-country skiing is a fairly safe activity, and historically the risk of injury has been relatively low. However, advances in equipment development, together with increasing speeds, more demanding trails, and growing numbers of participants, have all contributed to a larger report of injuries, although still comparatively low versus other skiing modalities. Injuries in cross-country skiing can occur either after a single traumatic event or in the setting of chronic repetitive microtrauma (i.e., overuse injuries).

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.302
Teacher spread0.296 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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