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Record W4416222458 · doi:10.1302/1358-992x.2025.13.072

DESCRIPTION OF SURGICAL TREATMENT METHODS FOR PERIPROSTHETIC JOINT INFECTION FOLLOWING TOTAL HIP ARTHROPLASTY FOR OSTEOARTHRITIS IN ONTARIO, CANADA

2025· article· en· W4416222458 on OpenAlexaffabout
Lauren L. Nowak, Emily Dawson, Ava John‐Baptiste, Emil H. Schemitsch

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Infections and Treatments
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsPeriprostheticOsteoarthritisLogistic regressionArthroplastyTotal hip arthroplastyDemographicsJoint arthroplastyComplication

Abstract

fetched live from OpenAlex

Periprosthetic joint infection (PPJI) is a serious and devastating complication following total hip arthroplasty (THA), and the optimal management of PPJI remains under debate. We sought to examine surgical treatment patterns and outcomes for PPJI following THA for osteoarthritis from 2012 to 2020 in Ontario, Canada. We used administrative databases to identify all patients who underwent THA for osteoarthritis from 2012 to 2020 in Ontario, Canada using procedural and diagnosis codes. We used the Canadian Joint Replacement Registry to identify patients who underwent revision surgery for PPJI, and categorized them based on type of revision surgery: a) Head and liner exchange (HL); b) 1 stage revision (1S); and c) 2 stage revision (2S). We further identified any repeat revision surgeries up to two years. We used Chi-square and Fisher exact tests to compare the unadjusted differences in patient demographics and outcomes between groups, and multivariable logistic regression to identify variables independently associated with revision surgery. We identified 67,267 patients who underwent THA between 2012 and 2020. Of these, 312 (0.5%) underwent revision surgery for PPJI, 126 (40.4%) in the HL group, 70 (22.4%) in the 1S group, and 116 (37.1%) in the 2S group. The proportion of patients in the 1S group increased from 22% in 2012 to 2014 to 35.5% in 2018 to 2020, the proportion of patients in the 2S group decreased from 40.7% in 2012 to 2014 to 20.8% in 2018 to 2020, and the proportion of patients in the HL group increased from 37.4% in 2012 to 2014 to 43.8% in 2018 to 2020. Variables independently associated with undergoing any type of revision surgery for PPJI included a higher CCI (Odds ratio 1.75, 1.12 to 2.72), a higher deprivation index (1.57, 1.05-2.37), and a longer wait time for their initial THA (1.01, 1.00-1.02). Patients in the HL group were more likely to be older, have a higher Charlson Comorbidity Index (CC), live further away from a hospital, and live in an area experiencing more material deprivation. Of the 312 patients who underwent revision for PPJI, 61 (19.6%) underwent a repeat revision within 2 years. The proportion of second revisions was non-significantly higher in the 2S group (26, 22.4%) compared to the 1S (14, 20.0%), and HL (21, 16.7%) groups. The majority of repeat revisions were performed due to infection in all groups (HL: 76.2%, 1S: 57.1%; 2S: 84.6%), followed by aseptic loosening (HL: 9.5%; 1S: 21.4%; 2S: 15.4%). While the proportion of patients undergoing 1 stage revisions for PPJI has increased in Ontario, Canada from 2012 to 2020, the risk of repeat revision surgeries remains high across all surgical management options for PPJI. These data suggest that patients with a higher CCI, with a higher deprivation index, or longer initial wait time (from referral to surgery) for initial THA may be at an increased risk of PPJI following THA for osteoarthritis. Prospective clinical studies are required to further examine the outcomes of revision surgery for PPJI.

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)0.004
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 score0.036
Threshold uncertainty score0.258

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.008
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.291
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 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
Published2025
Admission routes2
Has abstractyes

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