Risk factors for recurrence of periprosthetic joint infection following operative management: a cohort study with average 5-year follow-up
Bibliographic record
Abstract
Background: Periprosthetic joint infections (PJIs) remain challenging to eradicate even after surgical management, which in most cases involves either debridement, antibiotics and implant retention (DAIR) or single- or two-staged revision. The purpose of this study is to determine predictors of PJI recurrence after operative management for PJI, and to determine differences in recurrence-free survival between DAIR and staged revision. Methods: This is a retrospective analysis of prospectively collected data of revision hip and knee arthroplasty surgeries due to PJI between 2011 and 2018 at an academic hospital. Any patient undergoing revision surgery for PJI was included except if the index surgery information was unknown. The primary outcome was confirmed PJI recurrence. Multivariable logistic regression analysis was utilized to determine the relationship between the predictor variables and outcome variable. Log rank testing was used to compare recurrence-free survival between DAIR and staged revision. Results: A total of 89 patients (91 joints) underwent revision surgery due to PJI. Younger age and presence of a sinus tract were statistically significant for risk of PJI recurrence. A multivariable logistic regression model including both variables was significant for predicting recurrence of PJI (χ2=10.2, P=0.006). Survival was not significantly different between patients who underwent DAIR versus a staged revision. Conclusions: Younger patients and those with a chronic sinus tract are at significantly higher risk of recurrent PJI. This study also demonstrated that PJI can be successfully managed in the majority of cases with DAIR or staged revision.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".