TRENDS IN THE MICRO-ORGANISM PROFILES OF PERIPROSTHETIC JOINT INFECTION SINCE THE START OF THE 21ST CENTURY: A RETROSPECTIVE DATABASE STUDY
Bibliographic record
Abstract
Periprosthetic joint infection (PJI) is a catastrophic complication following total joint arthroplasty (TJA), with an incidence ranging from 1% to 2% in primary procedures. Despite various prevention strategies, the management of PJI remains challenging, leading to significant revision surgeries. Identifying the causative microorganism is crucial for effective treatment, but limited research exists on the temporal changes in microbial profiles of PJIs. This retrospective cohort study analyzed septic exchange hip and knee arthroplasty cases at a high-volume tertiary infection referral center in Europe between 2001 and 2022. PJI cases were identified using the institution's electronic joint infection database. Patients’ demographics, culture results, CRP values, and antibiotic sensitivity were collected. Among 2,392 patients with infected hips (60.7%) and knees (39.3%), Staphylococcus was the most common causative organism (60.6%), followed by Streptococcus (10.9%) and Enterococcus (7.8%). Gram-negative organisms accounted for 4.7% of cases, with fluctuating rates over time. Culture-negative cases comprised 10.6% of all infections. Polymicrobial infections increased over the study period, reaching almost 40% in 2022. Rates of difficult-to-treat bacteria rose significantly from 22.8% in 2017 to 53.0% in 2022, with methicillin-resistant Staphylococcus aureus being the most common resistant organism. The rise in resistant organisms and polymicrobial infections poses challenges in managing PJI. Improved diagnostic techniques, such as next-generation sequencing, may aid in identifying culture-negative cases and refining PJI management strategies. Effective antimicrobial stewardship is essential to combat growing antibiotic resistance in PJI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".