ReviewOrthopadie (Heidelberg, Germany)2026
[Robotic knee arthroplasty-an evidence-based, narrative assessment of the current state].
Review in Orthopadie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRobotically assisted knee arthroplasty (r-CAS) enables highly precise implant placement. However, current evidence shows no consistent advantage in clinical outcomes or implant survivorship. Scientific evaluation is challenged by both the technical heterogeneity of robotic systems and the rapid evolution of software, including AI-driven optimization algorithms. These differences affect workflow, accuracy, and outcomes, limiting study comparability. PROSPECTS: Future value will likely lie less in further improving precision and more in enabling personalized treatment through integration of kinematic analysis and intelligent decision-support models. This will require not only advanced hardware, software, and algorithms, but also new dynamic real-world evidence approaches to assess patient benefit.The present article provides an overview of the current state of evidence-based research for r-CAS and discusses approaches for bringing rapid technological advancements into balance with valid, patient-centered research in the future.
Indexed as
Identifiers
42742691What OpenQuestion holds
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.