Opportunities for early oral therapy for prosthetic hip and knee joint infections (PJI): clinical experience at a large health authority
Bibliographic record
Abstract
Objectives: We described the clinical outcomes and estimated cost savings from switching patients to early oral therapy from intravenous (IV) therapy for prosthetic joint infections (PJI) based on predefined criteria. Methods: Retrospective observational study in a large health authority consisting of 12 acute care hospitals in Canada. Patient demographics, microbiological and treatment data were collected for all patients with first episode of knee or hip PJI in 2022. Treatment failure rates, allergic or adverse reactions to IV or oral treatment, and hospital readmission rates were reported for those who met criteria for early switch to oral therapy. Results: Fifty-one patients were included. Thirty-seven patients (73%) had knee PJI, with debridement, antibiotics, and implant retention being the most common procedure. Sixteen patients (31%) had IV therapy for the entire duration of treatment, and the mean duration was 44 days. Twenty-three patients (45%) could have been switched to oral therapy. In practice however, only 3 patients (6%) were switched to oral therapy by day 7 following surgical source control. Five patients (22%) had clinical and/or microbiological failure 2 years postsurgery. Four patients (17%) and 6 patients (26%) developed an allergic or adverse reaction to IV and oral therapy, respectively. Five patients (22%) developed line complications. We estimated potential cost savings of almost $70,000 Canadian dollars with early oral therapy. Conclusion: Almost half of our PJI patients could have been switched to oral therapy within 7 days post-surgical source control. This study highlights a great opportunity for antimicrobial stewardship.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".