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Factors Affecting Survival of Uncemented Total Hip Arthroplasty in Patients 50 Years or Younger

2006· article· en· W2020317754 on OpenAlexaff
Stephen R. Kearns, B Jamal, C.H. Rorabeck, Robert B. Bourne

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2006
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineSurgeryTotal hip arthroplastyHip dysplasiaArthroplastySports medicineTotal hip replacementProsthesisImplantOrthopedic surgerySurvival analysisSurvival rateBearing surfaceOverall survivalRadiographyPhysical therapy

Abstract

fetched live from OpenAlex

Providing a long-lasting total hip arthroplasty for patients younger than 50 years remains one of the greatest challenges for modern arthroplasty surgery. We retrospectively reviewed 221 patients younger than 50 years who underwent 299 uncemented total hip arthroplasties from 1983 to 2000. We assessed 5- to 15-year survival with revision as the endpoint. Femoral stem survival was 99.3% (range, 98.4-100%), 98.9% (range, 97.7-100%), and 96.8% (92.5-100%) at 5, 10, and 15 years, respectively. Including all component designs acetabular survival was 98.7% (range, 97.4-100%), 84.6% (78.8-90.4%), and 52.5% (40.7-64.3%) at 5, 10, and 15 years, respectively. Overall survival was 46.8% (33.5-58.1%) at 15 years. Total hip arthroplasties performed for hip dysplasia had lower 10-year and 15-year survival. Zirconium-on-polyethylene articulations had lower acetabular revision rates compared with cobalt-chrome-on-polyethylene. Sixty-nine revisions were performed, most commonly for polyethylene wear. Uncemented femoral stems resulted in 90% survival at 15 years followup in patients younger than 50 years at index operation. Contemporary bearing surfaces in association with such stems may provide long-lasting total hip arthroplasties, even in young, active patients.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.015
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.088
GPT teacher head0.389
Teacher spread0.301 · 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

Citations47
Published2006
Admission routes1
Has abstractyes

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