Factors Associated with Success in Revision Surgery for Infected Hip and Knee Arthroplasties
Bibliographic record
Abstract
Abstract Background Patients with prosthetic joint infections (PJIs) often fail treatment. We aimed to describe the characteristics and outcomes of PJIs managed at a tertiary institution in Canada. Methods We assembled a cohort of patients undergoing surgical revision for hip or knee PJIs from January 1, 2010 until December 31, 2014 at our referral hospital by searching procedure descriptions from operative listings. Patient characteristics were abstracted by chart review. Treatment failure (TF) was defined by PJI recurrence requiring surgery, receipt of suppressive antimicrobials, amputation, excision or death. Results 243 individuals with a PJI undergoing a revision surgery were included. Median age was 69 years, 111 (46%) were males, 118 (49%) involved hips, 125 (51%) were knees, incision and drainage was performed in 53 (22%), a two-stage procedure was undertaken in 168 (69%), and a one-stage procedure in 18 (7%). Most PJIs were monomicrobial (50%); with coagulase negative staphylococci (35%) and Staphylococcus aureus (18%) the most common. TF occurred in 85/171 (47%): 53 (62%) required revision surgery, 23 (27%) chronic suppressive antimicrobials, 5 (6%) amputation, and 4 (5%) died (Table 1). On univariate analysis incision and drainage was associated with failure (OR 2.8, 95% CI 1.3–5.8, P = 0.002) while a two-stage procedure (OR 0.4, 95% CI 0.2–0.8, P = 0.009) and chronic symptoms (OR 0.4, 95% CI 0.2–0.8, P = 0.008) were protective. No risk factors for TF were identified on multivariable analysis. Conclusion PJIs are challenging to eradicate. New treatment paradigms are needed. Disclosures A. McGeer, Hoffman La Roche: Investigator, Research grant. GSK: Investigator, Research grant. Sanofi pasteur: Investigator, Research grant
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".