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Record W3185442394 · doi:10.21203/rs.2.20050/v1

Sociodemographic, health and fracture profiles of a 4-year cohort of 266,324 first incident upper extremity fractures in Ontario.

2020· preprint· en· W3185442394 on OpenAlexafffundabout
Joy C. MacDermid, J. Andrew McClure, Lucie Richard, Susan Jaglal, Kenneth J. Faber

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of TorontoWestern University
FundersLawson Health Research InstituteCanadian Institutes of Health ResearchSchulich School of Medicine and Dentistry, Western UniversityOntario Ministry of Health and Long-Term CareSchulich School of Medicine and DentistryAcademic Medical Organization of Southwestern Ontario
KeywordsCohortMedicineFracture (geology)DemographyPhysical therapyInternal medicineGeologyGeotechnical engineeringSociology

Abstract

fetched live from OpenAlex

Abstract Background The purpose of this study was to describe 1st incident fractures of the upper extremity in terms of fracture characteristics, demographics, social deprivation and comorbid health profiles. Methods:Cases with a 1st adult upper extremity fracture from the years 2013 to 2017 were extracted from administrative data in Ontario, (population 14.3M). Fracture locations (ICD-10 codes) and associated characteristics (open/closed, associated hospitalization within 1-day, associated nerve or tendon injury) were described by fracture type, age category and sex. Fracture comorbidity characteristics were described in terms of the prevalence of diabetes, rheumatoid arthritis; and the Charlson Comorbidity Index. Social marginalization was expressed using the Ontario Marginalization Index (ON-Marg) for material deprivation, dependency, residential instability, ethnic concentration. ResultsFrom 266,324 first incident UE fractures occurring over 4 years, 51.5% were in women and 48.5% were in men. This masked large differences in age-sex profiles. Most commonly affected were the hand (93K), wrist/forearm(80K), shoulder (48K) or elbow (35K). The highest number of fractures: distal radius (DRF, 47.4K), metacarpal (30.4K), phalangeal (29.9K), distal phalangeal (24.4K), proximal humerus (PHF, 21.7K), clavicle (15.1K), radial head (13.9K), and scaphoid fractures (13.2K). The most prevalent multiple fractures included: multiple radius and ulna fractures (11.8K), fractures occurring in multiple regions of the upper extremity (8.7K), or multiple regions in the forearm (8.4K). Fractures most common in 18 – 40-year-old men included metacarpal and finger fractures. A large increase in fractures in women over the age of 50 occurred for: DRF, PHF and radial head. Tendon (0.6% overall; 8.2% in multiple finger fractures) or nerve injuries (0.3% overall, 1.5% in distal humerus) were rarely reported. Fractures were open in 4.7%, highest for distal phalanx (23%). Diabetes occurred in 15.3%, highest in PHF (29.7%). Rheumatoid arthritis occurred more commonly in women (2.8% vs 0.8% men). The Charlson Index indicated low comorbidity (mean=0.2; median=0: 2.4% 3+), highest in PHF (median=0; 6.6% 3+). Higher fracture burden was related to instability (excess of fractures in lower 2 quartiles 4.8%), although social indices varied by fracture type. ConclusionsFracture specific prevention strategies should consider fracture-specific age-sex interactions, health, behavioural and social risks

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 score0.123
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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".

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Citations0
Published2020
Admission routes3
Has abstractyes

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