Evidence map›Paper›PMID 42474645›Full record

ArticleJournal of robotic surgery2026

Does technology increase the risk of stiffness after Total Knee Arthroplasty ? Evaluating the role of the learning curve.

Carmelo Burgio, Karlos E Zepeda, Tsion Yared, Fernando Quevedo Gonzalez, Peter K Sculco, David J Mayman, Seth A Jerabek, Jonathan M Vigdorchik, Eytan M Debbi

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Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Carmelo BurgioAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA. burgioc@hss.edu.ORCID http://orcid.org/0009-0006-6341-9564
Karlos E ZepedaAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
Tsion YaredAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
Fernando Quevedo GonzalezDepartment of Biomechanics, Hospital For Special Surgery, New York, USA.
Peter K SculcoAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
David J MaymanAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
Seth A JerabekAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
Jonathan M VigdorchikAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.
Eytan M DebbiAdult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arthrofibrosis and stiffness are challenging complications after total knee arthroplasty (TKA), often treated with Manipulation Under Anesthesia (MUA). Robotic-assisted TKA (RA-TKA) aims to enhance surgical precision, though its impact on stiffness remains unclear. This study examined the association between surgical technologies and MUA rates using propensity score matching (PSM) and assessed whether surgeon experience affects MUA risk. We conducted a retrospective case-control study of 25,611 primary unilateral TKAs (2016-2024), stratified by manual, computer-assisted (CA), or RA techniques. MUA cases within 90 days were matched 1:2 to controls using PSM. Conditional logistic regression assessed the association between surgical technologies and MUA risk. A learning curve analysis of each surgeon's first 100 RA-TKAs assessed surgical proficiency progression. Odds ratios were compared before and after the learning phase to evaluate its impact on MUA rates. Both CA-TKA (OR = 1.11, P = 0.33) and RA-TKA (OR = 1.24, P = 0.12) revealed a higher trend in MUA risk compared to manual. Learning curve analysis of 10 surgeons demonstrated a distinct learning breakpoint at case 11.8 (95% CI 9.2-14.4), after which MUA rates declined and stabilized. The pre-learning phase showed a 5.2% MUA rate, compared with 2.9% post-learning (P = 0.042). Adjusted analyses showed that the learning phase was independently associated with a 2.13-fold increase in MUA risk (95% CI 1.02-4.45, P = 0.043), suggesting that early robotic adoption may contribute to postoperative stiffness risk. Finally, after removing these early cases from the full cohort, RA-TKA showed no increased risk (OR 0.98; 95% CI 0.74-1.29), indicating no increased risk once surgeons were beyond the learning phase. Early robotic adoption may contribute to postoperative stiffness risk. Once surgeons progressed beyond the adoption phase, RA-TKA showed similar risk to other techniques, emphasizing the importance of structured training during robotic adoption.

Indexed as

Arthroplasty, Replacement, KneeLearning CurvePostoperative ComplicationsRobotic Surgical ProceduresAgedAnesthesiaCase-Control StudiesClinical CompetenceFemaleHumansMaleMiddle AgedPropensity ScoreRetrospective StudiesLearning curveManipulation under anesthesiaRobotic-assistedStiffnessTotal knee arthroplasty

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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.