Mortality Rates in Staged Bilateral Total Knee Arthroplasty: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to assess the mortality rates of staged bilateral total knee arthroplasty (staBTKA) procedures. This systematic review followed the suggestions and recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. We performed a primary search in the PubMed, EBSCO, Scopus, Web of Science via Clarivate, and Google Scholar databases. A quantitative data synthesis was conducted to estimate the pooled prevalence of mortality among patients who underwent staBTKA using the MedCalc® Statistical Software version 20.115 (MedCalc Software Ltd., Ostend, Belgium). The Newcastle-Ottawa Scale was used for the quality assessment. The study included 29 studies with data from 115,348 patients. The mortality rate was estimated to be 0.34% (95% confidence interval: 0.18% to 0.55%). The data showed significant interstudy heterogeneity (I2 = 93.3%). We used funnel plot inspection to visually assess significant publication bias, which showed a symmetrical distribution of the plotted data. Our study found that the mortality rates following staBTKA are relatively high. However, the reliability of our findings is limited due to significant heterogeneity. We recommend that further studies be conducted to provide higher-quality evidence to assess mortality in staBTKA and its trends.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.009 | 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".