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Record W2997602668 · doi:10.1097/jsm.0000000000000819

Do Mountain Bikers Know When They Have Had a Concussion and, Do They Know to Stop Riding?

2019· article· en· W2997602668 on OpenAlexaff
Gregory Clark, Nathalie A. Johnson, Sanjeet Singh Saluja, José A. Correa, J. Scott Delaney

Bibliographic record

VenueClinical Journal of Sport Medicine · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsConcussionMedicineInjury preventionPoison controlSuicide preventionOccupational safety and healthPhysical therapyHuman factors and ergonomicsDemographyMedical emergency

Abstract

fetched live from OpenAlex

OBJECTIVE: To establish the prevalence of concussions in mountain bikers and to determine factors that increase their risk of concussion. Secondary objectives include determination of whether mountain bikers have undiagnosed concussions, continue to ride after experiencing concussion symptoms, and if they knowingly ride with a broken helmet. DESIGN: Retrospective survey. SETTING: Seven-day mountain bike stage race. PARTICIPANTS: Two hundred nineteen mountain bikers. MAIN OUTCOME MEASURES: Number of rider concussions diagnosed, number of riders experiencing concussion symptoms without diagnosed concussions, number of riders who continue to ride after experiencing a concussion symptom, and number of riders who rode with a broken helmet. INDEPENDENT VARIABLES: The independent variables studied included age, gender, nationality, number of times riding in past year, style of riding (cross-country, downhill, or freeride), years mountain biking, years mountain bike racing, whether they are a sponsored cyclist, and whether they also ride a road bike. RESULTS: Fifteen of 219 mountain bikers (6.9%) had a diagnosed concussion after being hit in the head while mountain biking within the past year, with older riders having a decreased risk [odds ratio (OR), 0.91; P = 0.04], and sponsored riders having a 5-fold increased risk compared with nonsponsored riders (OR, 4.20; P = 0.05). Twenty-eight riders (12.8%) experienced a concussion symptom without being diagnosed with a concussion and 67.5% of the riders who experienced a concussion symptom continued to ride afterward. Overall, 29.2% of riders reported riding with a broken helmet. CONCLUSIONS: The yearly prevalence of diagnosed concussions in mountain bikers is 6.9%. More than one-third of mountain bikers do not recognize when they have had a concussion and continue riding after experiencing concussion symptoms or with a broken helmet. These behaviors increase their risk of worsening concussion symptoms and acquiring a second injury.

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)0.004
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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