ASSOCIATION BETWEEN PROSTHETIC JOINT INFECTION AND MORTALITY FOLLOWING PRIMARY TOTAL HIP ARTHROPLASTY
Bibliographic record
Abstract
Prosthetic joint infection (PJI) remains a dreaded and unpredictable complication after total hip arthroplasty (THA). In addition to causing significant morbidity, PJI may contribute to long-term mortality risk. Our objective was to determine the long-term mortality risk associated with PJI after THA. We conducted a population-based retrospective cohort study of adult patients (age > 18 years) in Ontario, Canada who underwent primary elective total hip arthroplasty for arthritis between April 1, 2002 and March 31, 2021. Main outcomes were death within ten years of index joint replacement. Outcomes were compared for propensity-score matched groups (PJI within one year of surgery versus no-PJI within one year of surgery), using survival analyses. Patients who died within one year of surgery were excluded to avoid immortal-time bias. A secondary analysis was performed solely in patients who had a PJI within one year of THA to identify predictors of death within ten years. A total of 175,432 patients (mean [SD] age 67 [11.4] years; 95,883 (54.7%) women) had a total hip replacement during the study period. Of these, 868 patients (0.49%) underwent surgery for a PJI of the replaced joint within one year of their index procedure. After matching, patients who had an infection in the first year had a significantly higher ten-year mortality rate (94 [11.4%] vs 18 [2.2%]; absolute risk difference (RD) 9.19% [95% Confidence interval (CI) 6.81%-11.6%]; HR 5.49 [95% CI 3.32-9.09]) (Figure 1). Among those with PJI, factors associated with greater risk of mortality were COPD (HR 3.85, p Prosthetic joint infection within a year of surgery is associated with over a 5-fold increased ten-year risk of mortality. Among those with PJI, risk of mortality was further increased by COPD, diabetes with complications, liver disease, and frailty. The findings of this study underscore the importance of prioritized efforts relating to the prevention, diagnosis, and treatment of PJIs. For any figures or tables, please contact the authors directly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".