Incidence of Postoperative Infection Following Simultaneous Bilateral Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Total knee arthroplasty is one of the most common orthopedic procedures. Simultaneous bilateral knee arthroplasty involves performing total knee arthroplasty on both knees in a single anesthetic session. This systematic review and meta-analysis followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020. A primary search was performed using PubMed, EBSCO, Scopus, Web of Science, Clarivate, and Google Scholar databases. Quantitative data synthesis was performed using MedCalc® Statistical Software version 20.115 to determine the pooled prevalence of the infection among patients who underwent simultaneous bilateral knee arthroplasty. The Newcastle-Ottawa Scale was used to assess study quality. We included 30 studies in our quantitative data synthesis, with a total population of 118,502 patients (237,004 knees). The pooled prevalence of superficial infection, deep infection, and unspecified surgical site infection was estimated to be 0.86% (95% confidence interval: 0.62-1.13%), 0.84% (95% confidence interval: 0.64-1.05%), and 1.18% (95% confidence interval: 0.45-2.27%), respectively. There was significant heterogeneity (I2 >50%) in all analyses, and inspection of funnel plots revealed a symmetrical distribution of plotted data. We found that the infection rates following simultaneous bilateral knee arthroplasty were relatively low but heterogeneous, as the data showed marked variability. Superficial infections were more common than deep infections; however, there was a small difference in their prevalence. Furthermore, the reliability of our findings was limited owing to significant heterogeneity.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".