Editor’s Spotlight/Take 5: CORR Synthesis: What Is the Role of Robotic-assisted Technology in Knee Arthroplasty?
Seth S. Leopold
- Year
- 2025
- Citations
- 5
Abstract
Randall E. Marcus MDFor this Editor’s Spotlight commentary, I’m going to try something different. There will be no references in the text, other than to the article I’m commenting on [3]. It’s not that we lack good studies on the topic in question, quite the contrary—it’s that those studies don’t seem to be influencing the conversation to any great degree, so let’s dispense with them altogether. Let’s talk about our feelings. The topic is robotic-assisted joint replacement, but of course the larger issue—which will make what follows relevant to all surgeons, not just arthroplasty subspecialists—is how, when, and why we integrate new technology and tools into our practices. Surgeons who spend a lot of time with noses in clinical research journals point to the near-complete lack of high-quality evidence suggesting that robotic-assisted arthroplasty delivers benefits that patients can perceive. The wide adoption in practice of these tools, when held against the available evidence in favor of them, causes those surgeons to feel a dissonance. But it’s a two-way street. When I speak with surgeons who perform robotic-assisted arthroplasty, they consistently say that it has improved their surgical skills. They say that the experience of using a robot teaches the surgeon subtleties about the anatomy and joint kinematics that would be hard otherwise to perceive but are nonetheless important. Speaking of feelings, those surgeons also say that the joints “feel” better—as in more stable hips and more balanced knees—and their patients’ recoveries are quicker and more complete. Those surgeons feel a dissonance because these things seem obvious, and in some instances even dramatic, but these benefits haven’t turned up in the randomized clinical trials (RCTs) performed thus far on the topic. There is no doubt in my mind that these surgeons’ feelings are authentic and deeply held. Importantly, they may also be correct. One way or the other, two groups of surgeons seem to be talking past each other. Let’s see if we can find a way to talk with one another. In this month’s Editor’s Spotlight/Take 5 article, “CORR Synthesis: What Is the Role of Robotic-assisted Technology in Knee Arthroplasty?” [3], the authors from Case Western Reserve University in Cleveland, OH, USA, performed a thorough vetting of the available RCTs on the topic and found that knee alignment parameters often improved with use of robotic surgical systems, but those improvements seldom were associated with clinically important differences in endpoints patients can perceive, like patient-reported outcomes or complications. Only one study followed patients long enough to evaluate survivorship differences, but at 10 years, it found no benefit to robotic TKA in this endpoint. The cliché of “the absence of evidence isn’t evidence of absence” may be relevant here, but perhaps equally applicable is the old saw, “the plural of anecdote isn’t data.” Either way, this topic prompts tough questions in both directions, even as it may inflame the passions. It’s the kind of thing, though, that sensible people ought to be able to work out in conversation, so let’s have one. And, again, this conversation should be of interest to people who don’t do joint replacement as well as those who do, since the themes relate broadly to the question of when to adopt new approaches, which is a question that is of interest to all of us. As an aside, the senior author on the article I’m covering here, Randall E. Marcus MD, has had a long career in this subspecialty and a long tenure as the chair of a large department. Importantly, he’s also been a thoughtful observer of trends in our profession for 45 years. He’s great at seeing things from both sides, so he is the right guy to explore big questions with. Before we go to that interview, a quick plug: If you haven’t seen the CORR Synthesis article type before, have a look at Dr. Marcus’s article [3] in this issue (it follows directly after the interview in the T
Keywords
Related papers
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi +7 more
2021
3D is here: Point Cloud Library (PCL)
Radu Bogdan Rusu, Steve Cousins
2011
Causal Diagrams for Epidemiologic Research
Sander Greenland, Judea Pearl, James M. Robins
1999
Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig, A. Howard
2005