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Record W3199505846 · doi:10.2106/jbjs.21.00346

Unicompartmental Knee Arthroplasty: Survivorship and Risk Factors for Revision

2021· article· en· W3199505846 on OpenAlexaffabout
Seper Ekhtiari, Anthony Bozzo, Kim Madden, Mitchell Winemaker, Anthony Adili, Thomas J. Wood

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2021
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsHamilton Health SciencesSt. Joseph’s Healthcare HamiltonMcMaster University
Fundersnot available
KeywordsUnicompartmental knee arthroplastyMedicineSurvivorship curveArthroplastyOsteoarthritisConfidence intervalPopulationSurgeryCohortProportional hazards modelInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Operative management for end-stage knee osteoarthritis (OA) primarily consists of arthroplasty. Total knee arthroplasty (TKA) is the so-called gold standard for multicompartmental OA. In selected patients, specifically those with unicompartmental OA and no definite contraindications, realignment osteotomies and unicompartmental knee arthroplasty (UKA) are viable options. UKA offers several advantages over TKA, including preservation of both cruciate ligaments, faster recovery, and less osseous resection. The purpose of this study was to determine the survivorship of UKAs and risk factors for all-cause revision surgery in patients with a minimum follow-up of 10 years in a large population-based database. METHODS: We performed a population-based cohort study using linked administrative databases in Ontario, Canada. We identified all UKAs performed from January 2002 through December 2006. All patients had a minimum 10-year follow-up. Demographic data and outcomes were summarized using descriptive statistics. We used a Cox proportional hazards model with the Fine and Gray method accounting for competing risks such as death to analyze the effect of surgical and patient factors on the risk of revision of UKA. RESULTS: A total of 4,385 patients were identified as having undergone primary UKA from 2002 through 2006 in Ontario. Of these patients, 779 underwent revision surgery, for a cumulative risk for all-cause revision of 16.5% at 10 years (95% confidence interval [CI]: 15.4% to 17.7%). Mechanical loosening was the reason for 83.4% of all revisions. Male sex, diabetes, an age younger than 50 years, and cementless implants were significant risk factors for revision surgery. Residence in a rural or urban setting, income quintile, discharge destination, and type of OA were not associated with the risk of revision. Survivorship at 1, 5, 10, and 15 years was 97.2%, 90.5%, 83.5%, and 81.9%, respectively. CONCLUSIONS: This study demonstrated a >80% long-term survivorship for UKA based on a large administrative database sample and identified important risk factors for failure including male sex, younger age, diabetes, and cementless fixation. This study found rates of survivorship similar to those previously reported in the literature, while identifying key risk factors for failure. Patient selection has always been identified as a key factor in ensuring successful UKA, and the risk factors identified in this study may help guide decision-making for surgeons and patients dealing with unicompartmental knee OA. LEVEL OF EVIDENCE: Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

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)0.002
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 score0.080
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.264
Teacher spread0.227 · 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

Citations34
Published2021
Admission routes2
Has abstractyes

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