Outcomes of Long-Term Antibiotic Therapy for Prosthetic Joint Infection: A Cohort Study in a Canadian High-Volume Arthroplasty Center
Bibliographic record
Abstract
Background Long-term antibiotic therapy (LAT) can be used to prevent relapse of prosthetic joint infection. Evidence on best practices for LAT is limited and conflicting. We aimed to assess total hip or knee arthroplasty revision for prosthetic joint infection for patients on LAT. Methods We identified a single-institution study cohort of patients who used oral antibiotics for longer than 90 days. We then performed a chart review to identify demographic and clinical characteristics of these patients at the index revision and the day LAT ended. We used Cox proportional hazard models to estimate the hazard ratios (HRs) of the association between these characteristics and later revision. Results Our study cohort consisted of 85 patients (87 joints), 60% were male; median age was 65 years, and median body mass index was 32 kg/m 2 . One-third of infections were caused by staphylococci and one-third were culture-negative. Above-median (≥30 mg/L) preoperative C-reactive protein was associated with a higher risk of subsequent revision, HR=4.5 (95% confidence interval 1.1-18.8). Staphylococcus aureus infection (compared to other staphylococcal infections) did not seem to be associated with an increased risk of revision, HR=1.3 (95% confidence interval 0.3-6.4). LAT ended for 70% of patients; we identified no specific risk factors for post-LAT revision. Conclusions Elevated C-reactive protein levels seem to be consistently associated with an increased risk of re-revision. We could not assess whether there is a threshold above which patients are at increased risk. Other factors, for example, increased age, may play a role, but we were unable to rule definitively on these factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".