SynthesisArchives of orthopaedic and trauma surgery2025
Cutting through infection risk: robotic-assisted vs. conventional total knee replacement surgery - a meta-analysis.
Synthesis in Archives of orthopaedic and trauma surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Trends and Outcomes in Technology-Assisted Total Knee Arthroplasty in the United States From 2020 to 2024.Arthroplasty today · 2026Article
- Risk of periprosthetic joint infection within 1 year following robotic-assisted versus conventional primary total knee arthroplasty: a propensity-score-matched cohort study.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2026Article
- Recovery after Total Knee Arthroplasty: Does one or two knees matter?Journal of clinical orthopaedics and trauma · 2025Article
- Challenging the neutral axis: 15-year results of under-corrected varus in total knee arthroplasty.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025Article
- Hypertonic dextrose prolotherapy in osteoarthritis: mechanisms, efficacy, and future research directions.Frontiers in endocrinology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSurgical site infections (SSI) following knee replacement pose significant challenges, often attributed to microorganism contamination during surgery. Robotic-assisted total knee replacement (RA-TKR) offers potential advantages but may also raise concerns regarding infection risk. This systematic review and meta-analysis aimed to evaluate infection rates following RA-TKR compared to Conventional Total knee replacement (C-TKR) within the first-year post-implantation. MATERIALS AND
methodsA systematic search of major databases was conducted from 2005 to November 2023. Eligible studies reported primary C-TKR and RA-TKR interventions, documented SSI incidence, and had a follow-up period of at least 12 months. Data on deep, superficial, and pin-site infections were extracted, and the risk of bias was assessed using the Cochrane Risk of Bias Tool. Summary estimates were generated using a random-effects model.
resultsEight studies were included, comprising 758,453 knees (6724 RA-TKR, 751,729 C-TKR). The overall SSI rate was 1.57% in conventional TKR and 1.29% in RA-TKR. Deep infection rates were 0.96% and 0.66% in conventional and robotic procedures, respectively, with no significant variation between them (Odds Ratio [OR] 1.27 [95% Confidence Interval [CI], 0.93 to 1.73], I2 = 0%, P-value = 0.13). Superficial and pin-site infection rates were comparable between both procedures (0.61% vs. 0.62%) with no significant difference (OR 1.21 [95% CI, 0.86 to 1.70], I2 = 0%, P-value = 0.28).
conclusionThis meta-analysis shows low SSI rates for both RA-TKR and C-TKR within the first year post-implantation. Both techniques are viable with low SSI incidence, and further research should investigate longer-term outcomes and different robotic systems to refine infection risk understanding in knee arthroplasty.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.