Evidence map›Paper›PMID 40678787›Full record

ArticleArthroplasty today2025

Robotic Guided Knee Arthroplasty - Group Learning Curve and Early Outcomes.

Max Gordon, Gurion Rivkin, Alex Greenberg, Leonid Kandel, Meir Liebergall, Itay Perets

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Max GordonOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.
Gurion RivkinOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.
Alex GreenbergOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.
Leonid KandelOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.
Meir LiebergallOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.
Itay PeretsOrthopedic surgery department, Hadassah Medical Organization, Hadassah University Hospital, Israel, Jerusalem.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Previous studies have reported initial and proficiency phases of the learning curve among individual surgeons when robotic-assisted total knee arthroplasty (raTKA) into their practices. The purpose of this study was to assess the number of raTKA cases needed to achieve proficiency in the operative time (OT) across a group of surgeons. Methods: A retrospective analysis comparing raTKA to manual Total Knee Arthroplasty (mTKA) was performed at a single-site comprised of 6 orthopaedic surgeons. The first 90 patients who underwent raTKA at the institution divided chronologically into 3 groups of 30 each and compared to 30 previous mTKA cases. Tourniquet time was used as the measure for OT, range of motion (ROM) was measured preoperatively and at 2 and 6 weeks postoperative. Results: Average OT was significantly shorter in the mTKA group in comparison with all the raTKA groups. A statistically significant difference was found in OT when comparing the succeeding raTKA groups (mean time of 97.6, 86.6, and 76.7 min Conclusions: When evaluating the group learning curve, this study showed average shorter operating time with every 30 cases (mean time of 97.6; 86.6- and 76.7-min

Indexed as

Knee arthroplastyLearning curveRobotic assisted surgery

Identifiers

PMID40678787
PMCPMC12269883

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.