Evidence map›Paper›PMID 40904339›Full record

ArticleGeburtshilfe und Frauenheilkunde2025

Introduction of Robot-assisted Surgery for Benign Total Hysterectomy with Salpingectomy: Learning Curve, Safety and Experience in a Tertiary Surgical Center.

Lisa Jung, Benedikt Kurz, Peter Jungmann, Maximilian Klar, Sarah Isabelle Huwer, Florin-Andrei Taran, Angeline Favre-Inhofer, Markus Medl, Markus Hübner, Ingolf Juhasz-Böss

Abstract read
In one paragraph

Article in Geburtshilfe und Frauenheilkunde, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

10 authors.

Lisa JungKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Benedikt KurzKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Peter JungmannKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Maximilian KlarKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Sarah Isabelle HuwerKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Florin-Andrei TaranKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Angeline Favre-InhoferKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Markus MedlKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.
Markus HübnerKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.ORCID 0000-0003-4906-7751
Ingolf Juhasz-BössKlinik für Frauenheilkunde, Medizinische Fakultät, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The dynamic advances in robot-assisted surgery have particularly affected gynecological surgery. An analysis of the surgical data of robot-assisted procedures, starting when the procedure is first introduced into a surgical center, provides valuable insights into the initial use and integration of the da Vinci system in routine clinical practice and the impact on patient care. This article aims to investigate the learning curve and show the increased proficiency with this approach. This analysis focuses on the most common procedure performed during the introductory phase: benign total hysterectomy with salpingectomy. Material and Method: A retrospective data analysis was carried out of the first 250 patients operated on between February 2020 and June 2022 by five different surgeons in Freiburg University Hospital using the da Vinci surgical system. The evaluation includes classic surgical parameters such as preparation times, incision-to-suture times, and console times as well as the learning curves of the surgeons and the surgical team (incl. CUSUM analysis). Perioperative patient characteristics (e.g., blood loss, hospitalization times, conversion rate) are also presented. Results: Most procedures (30%) were carried out for uterine fibroids. Operating times decreased significantly over time as more and more robot-assisted procedures were carried out: the surgical preparation time decreased over the first 30 procedures from 28.1 ± 8.6 min to 23.8 ± 7.2 min. The initial incision-to-suture time for benign total hysterectomies with salpingectomy was 94.0 ± 42.2 min and had decreased significantly by the end of the first 20 procedures. The average console time was 66.8 ± 36.1 min, and the decrease was particularly visible over the first 20 procedures. The individual learning curves of the surgeons showed significant decreases in time. For example, the average console time of surgeon A decreased over the first ten procedures from 70.5 ± 23.0 min to 46.9 ± 13.5 min. The conversion rate for the whole cohort was 0.8%. Discussion: The evaluation of the first 250 da Vinci surgeries demonstrates the easy learnability of robot-assisted surgery. The conversion rate was very low, coming in at just 0.8%. A positive effect on the learning curve of individual surgeons was found after about 20 procedures. Both the preparation times and the incision-to-suture times decreased rapidly, meaning that there were no problems integrating the new approach into routine clinical practice.

Indexed as

console timeconversion rateda Vincilearning curverobot-assisted surgery

Identifiers

PMID40904339
PMCPMC12404793

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.