Comparison of Microbiological Profiles of Primary Hip and Knee Peri-Prosthetic Joint Infections Treated at Specialist Centers Around the World
Bibliographic record
Abstract
Periprosthetic joint infection (PJI) is a complex complication of total joint arthroplasty, with microbiological profiles varying across centers worldwide. However, most studies are limited to single-center or intra-country multicenter analyses, often including mixed cohorts of primary and revision PJI cases, with limited data regarding global antibiotic resistance patterns. This study compared the microbiological characteristics, polymicrobial culture rates, prevalence of culture-negative infections, and antibiotic resistance patterns in PJI cases across five referral centers from five continents. A total of 717 patients with primary hip and knee PJI were included from centers in Argentina, Canada, Turkey, England, and New Zealand. Staphylococcus aureus and Staphylococcus epidermidis were the most common pathogens (48.5%, p < 0.01). Culture-negative infection rates varied significantly, ranging from 4.2% (England) to 24.6% (Turkey) (p < 0.01). Polymicrobial infections were the most frequent in Canada (8.9%) and the least frequent in England (1.1%) (p < 0.01). Gram-negative bacteria comprised 13.1% of culture-positive cases, with no significant intercountry difference. Multidrug resistance was observed in all centers, ranging from 23.7% (Argentina) to 43.1% (Turkey), with no statistical significance. Vancomycin resistance was detected in England (2.3%) and Canada (1.2%) but absent in Turkey, New Zealand, and Argentina. These findings underscore significant intercontinental variability, emphasizing the need for regional considerations in regards to empiric antibiotic selection and PJI management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".