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Record W2150620028 · doi:10.1080/17457300.2010.540329

Do overweight and obese youth take longer to recover from injury?

2011· article· en· W2150620028 on OpenAlexafffundabout
Joel Warsh, Ian Janssen, William Pickett

Bibliographic record

VenueInternational Journal of Injury Control and Safety Promotion · 2011
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsOverweightMedicineObesityHazard ratioInjury preventionPopulationPoison controlOccupational safety and healthPhysical therapyDemographyGerontologyMedical emergencyInternal medicineEnvironmental healthConfidence intervalPathology

Abstract

fetched live from OpenAlex

The objective of the study was to examine the effects of overweight and obesity on times to recovery among Canadian youth who have suffered one or more types of injury. The data source was the 2002 Canadian Health Behaviour in School-Aged Children (HBSC) survey. The study population included 7266 youth in grades 6 through 10 sampled from all Canadian provinces and territories. Of these, 2831 students reported an injury event and were included in the analysis. Kaplan-Meier curves and hazard ratios (HR) were used to profile survival functions and estimate relative hazards for non-recovery from injury events among normal weight, overweight and obese youth. Youth who were obese and suffered a combined injury (broken bone and strain/sprain) took longer to recover (HR: 1.81, 95% CI 0.99-3.32) compared to normal weight youth. HR for injury recovery in obese youth were not significantly elevated for broken bones (1.15, 95% CI 0.61-2.19) and sprain/strains (1.17, 95% CI 0.73-1.85) in isolation. Obesity was associated with times for injury recovery among injured youth. If these findings are confirmed in other settings, clinicians providing an injury recovery prognosis may need to take into account BMI status and allow for extra recovery time for patients in this age range.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.496

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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

Citations3
Published2011
Admission routes3
Has abstractyes

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