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Record W4415438825 · doi:10.1302/1358-992x.2025.10.133

VARIATION IN EARLY MAJOR COMPLICATION RATES FOLLOWING TOTAL HIP ARTHROPLASTY BETWEEN HOSPITALS IN ONTARIO: A POPULATION-BASED STUDY

2025· article· en· W4415438825 on OpenAlexaboutno aff
David W. Pincus, Jeffrey D. Gollish, Bheeshma Ravi

Bibliographic record

VenueOrthopaedic Proceedings · 2025
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsComplicationLogistic regressionRetrospective cohort studyArthroplastyCohortTotal hip arthroplastyCohort studyOsteoarthritis

Abstract

fetched live from OpenAlex

Although early major surgical complications following total hip arthroplasty (THA) occur rarely, complications are significant for those that experience them. As discussed in the 2020 Report on Early Revisions of Hip and Knee Replacements in Canada , early major surgical complications “are more likely to be due to conditions surrounding the surgery or to the surgery itself…can be viewed as largely avoidable, and represent actionable opportunities to improve quality of care [and] increase health system productivity and capacity.” The purpose of this study was to determine the influence of hospital-level surgical practices on early major surgical complications across Ontario. We conducted a population-based retrospective cohort study of all adults in Ontario, Canada who had undergone primary THA for osteoarthritis between April 1, 2008 and March 31, 2019. Patients treated at hospitals completing fewer than 200 THAs during the study period were excluded. All patients were followed for one year (study end date March 31, 2020 i.e. prior to the COVID-19 pandemic). The primary outcome was early major surgical complications defined as a composite of deep infection requiring surgery, dislocation requiring closed or open reduction, or revision surgery occurring within 1 year of surgery. Medical complications occurring within 30 days of surgery (PE/DVT, MI, pneumonia) were also assessed. The random effects output from two-level hierarchical logistic regression models adjusted for age, sex and Charlson score were used to calculate each hospital's unique adjusted complication rate and 95% CI. Hospitals that had significantly different adjusted complication rates from the average were defined as statistical ‘outliers’ as follows: 1) ‘Low outliers’ as those with the upper limits of their 95% CI less than the mean cohort rate and 2) ‘High outliers’ as those with a lower limit of their 95% CI greater than the mean cohort rate. During the study period, 95,912 patients (mean [SD] age 67 [11.0] years; 51,216 (53.4%) women) underwent THA at 56 hospitals across Ontario. 1,656 (1.7%) patients had a major surgical complication within 1 year. Major surgical complication rates varied 7-fold between hospitals from 0.6% to 4.1%. After adjustment, 4 of 56 hospitals were low outliers (adjusted complication rate significantly lower than the average) and 5 of 56 were high outliers (adjusted complication rate significantly higher than the average). In contrast, there were no hospital outliers for medical complications (i.e. medical complications were not significantly different between hospitals). There was significant variation in early major surgical complication rates between Ontario hospitals that persisted after adjustment for patient age, sex and medical comorbidity. That we observed significant hospital-level variation in surgical but not medical complications suggests differences in surgical practices at different hospitals contributes to outcomes in addition to case mix alone. Feeding back adjusted outcomes in benchmarking reports may enable individual hospitals and surgeons better consider their own performance and scale up best practices. Future research should try understand causes of variable complication rates between hospitals and whether variability extends to PROMs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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