Evidence map›Paper›PMID 40256312›Full record

ArticleArthroplasty today2025

Improved Efficiency and Intraoperative Planning With 1 Robot-Assisted Total Knee Arthroplasty System.

Dalton L Braathen, Cameron Wallace, Ian M Clapp, Brenna E Blackburn, Christopher L Peters, Michael J Archibeck

Abstract read
In one paragraph

Article in Arthroplasty today, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Dalton L BraathenDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
Cameron WallaceDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
Ian M ClappDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
Brenna E BlackburnDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
Christopher L PetersDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.
Michael J ArchibeckDepartment of Orthopaedics, University of Utah, Salt Lake City, UT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robotic-assisted total knee arthroplasty (rTKA) has garnered significant interest for its potential to enhance surgical precision and accuracy. However, the adoption of such systems poses concerns, including longer operative times and learning curves, potentially reducing efficiency. This study aimed to evaluate the learning curve associated with the Robotic Surgical Assistant (ROSA) system for rTKA. Methods: This retrospective review analyzed the first 75 ROSA rTKA procedures performed by each of 2 fellowship-trained arthroplasty surgeons (150 total procedures) at a high-volume institution. Time stamps within the robotic software were recorded for each case, along with tourniquet time. Statistical analyses included descriptive statistics, Results: Comparison of each surgeon's first 20 and last 20 cases revealed significant decreases in tourniquet time (61.4-56.7 minutes; Conclusions: This study demonstrates that execution of ROSA rTKA becomes more efficient over the first 75 cases. The greatest improvement with experience is the time spent on the planning panel, the cognitive portion of the procedure. These data provide surgeons with the confidence that the technical portions of the case are quick to learn and guide industry to focus on teaching effective adjustments on the planning panel.

Indexed as

Learning curveRobotic-assisted surgeryROSATimeTotal knee arthroplasty

Identifiers

PMID40256312
PMCPMC12008555

What OpenQuestion holds

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Registered trials

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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.