Evidence map›Paper›PMID 39340627›Full record

ArticleJournal of robotic surgery2024

Learning curve of robotic assisted microsurgery in surgeons with different skill levels: a prospective preclinical study.

Donata von Reibnitz, Andrea Weinzierl, Lisanne Grünherz, Pietro Giovanoli, Nicole Lindenblatt

Abstract read
In one paragraph

Article in Journal of robotic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 2 pooled it
–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

9 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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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

5 authors.

Donata von ReibnitzDepartment of Plastic and Hand Surgery, University Hospital Zurich (USZ), Zurich, Switzerland.ORCID http://orcid.org/0000-0002-6599-9818
Andrea WeinzierlDepartment of Plastic and Hand Surgery, University Hospital Zurich (USZ), Zurich, Switzerland.ORCID http://orcid.org/0000-0003-2449-2698
Lisanne GrünherzDepartment of Plastic and Hand Surgery, University Hospital Zurich (USZ), Zurich, Switzerland.ORCID http://orcid.org/0000-0002-3559-1061
Pietro GiovanoliDepartment of Plastic and Hand Surgery, University Hospital Zurich (USZ), Zurich, Switzerland.
Nicole LindenblattDepartment of Plastic and Hand Surgery, University Hospital Zurich (USZ), Zurich, Switzerland. nicole.lindenblatt@usz.ch.ORCID http://orcid.org/0000-0003-0293-1004

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving precision in microsurgery requires skill, adequate instruments and magnification, as well as extensive training. Dedicated surgical robotic systems have enhanced and expanded the application of (super-)microsurgical techniques by introducing motion scaling and providing improved surgeon ergonomics. In this prospective preclinical trial, we analyzed the learning curve in robotic assisted microsurgery in 13 participants including medical students, residents, and attending physicians. Data on demographics as well as prior experience in surgery, microsurgery, and gaming were collected. In three study sessions, the participants performed nine microsurgical anastomoses each on 2 mm vessel models using the Symani

Indexed as

Clinical CompetenceLearning CurveMicrosurgeryRobotic Surgical ProceduresSurgeonsAdultAnastomosis, SurgicalFemaleHumansInternship and ResidencyMaleProspective StudiesStudents, MedicalMicrosurgeryMicrosurgery learning curveMicrosurgery trainingRobotic assisted microsurgerySurgical robotic system

Identifiers

PMID39340627
PMCPMC11438828

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.