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Record W2036984273 · doi:10.1177/2325967113s00013

Shoulder Dislocation In Ontario, Canada From 1994 To 2011: The Incidence, Rate And Risk Factors For Recurrence

2013· article· en· W2036984273 on OpenAlexaffabout
Timothy Leroux, David Wasserstein, Tim Dwyer, Christian Veillette, Amir Khoshbin, Rajiv Gandhi, Peter C. Austin, Nizar N. Mahomed, Darrell Ogilvie‐Harris

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2013
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsToronto Western HospitalInstitute for Clinical Evaluative SciencesMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCohortIncidence (geometry)Hazard ratioConfidence intervalSurvivorship curveDemographyDislocationSurgerySpecialtyPopulationInternal medicineFamily medicine

Abstract

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Objectives: Recurrent shoulder dislocation is influenced by age, activity level and bone loss. Original estimates of recurrence risk approached 90% among persons under the age of 20, but declined with increasing age. Recent literature, however, suggests that the rate of recurrent dislocation is lower. The goals of this study were to: (1) define the incidence of primary shoulder dislocation in Ontario, Canada, and (2) identify the rate of and risk factors for recurrent dislocation among demographic variables. Methods: Administrative databases (OHIP) were used to build the cohort of patients aged 15 to 70 that underwent a primary closed shoulder reduction by a physician in Ontario between July 1994 and October 2009. Exclusions included: associated humeral neck fracture, posterior dislocation, previous shoulder dislocation, prior shoulder arthroplasty, and non-Ontario residents. After cohort entry, subsequent shoulder relocations by a physician were sought. The yearly incidence (per 100,000 person-years) was calculated among all eligible Ontario residents. Kaplan-Meier survival curves to subsequent dislocation were generated. A Prentice, Williams and Peterson conditional proportional hazards survivorship model of time-to-recurrence was applied examining the influence of age, gender, income quintile, physician specialty and concurrent tuberosity fracture at index dislocation on the risk of recurrence (alpha set at 0.05). Hazard Ratios (HR) with confidence intervals were calculated. Results: The primary dislocation cohort consisted of 37,356 patients. Median age was 34 years (IQR 22-50) and 74% were male. The average yearly incidence of primary shoulder dislocation was 23.1/100,000 person-years overall, but 45.2/100,000 person-years for patients younger than 20. Recurrent dislocation events were identified in 23% of the cohort (8573 patients), most of whom were younger (median age 24 years) and male (80%). In fact, patients younger than 20 had a 37.8% rate of recurrence (HR 1.9 (1.7-2.1), p<0.0001; compared to patient aged 36-40). Kaplan-Meier survival curves showed most recurrent dislocations took place in the first year: 93.4% at 6-months, 89.4% at 1-year, 85.2% at 2-years and 79.4% at 5-years. Protective factors against recurrence included primary relocation performed by an Orthopaedic Surgeon (HR 0.87 (0.79-0.94), p=0.001), age over 50 years (5.5% rate; HR 0.70 (0.61-0.80, p<0.0001) and an associated tuberosity fracture (HR 0.51 (0.42-0.63), p<0.0001), while lowest income quintile was a risk factor for recurrence (HR 1.1 (1.04-1.16), p=0.0007). Interestingly, at 2.5 years from the primary dislocation, only 14.7% of the cohort had undergone surgical shoulder stabilization. Conclusion: Patients under 20 had twice the incidence of primary dislocation and twice the risk of recurrent shoulder dislocation compared to the median cohort age. A recurrence rate of 38% in patients under 20 is high, but less than previous reports.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.024
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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
Published2013
Admission routes2
Has abstractyes

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