Evidence map›Paper›PMID 41667699›Full record

ArticleArchives of gynecology and obstetrics2026

Learning curves, safety, and experiences of a tertiary surgical center in the introduction of robotic-assisted surgery in gynecologic oncology.

Lisa Jung, Florin-Andrei Taran, Sarah Huwer, Benedikt Kurz, Maximilian Klar, Angeline Favre-Inhofer, Ingolf Juhasz-Böss

Abstract read
In one paragraph

Article in Archives of gynecology and obstetrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

7 authors.

Lisa JungDepartment of Obstetrics and Gynecology at the Medical Center, University of Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany. lisa.jung@uniklinik-freiburg.de.
Florin-Andrei TaranDepartment of Gynecology and Gynecologic Oncology, Center of Integrated Oncology Aachen Bonn Cologne Düsseldorf, Medical Faculty and University Clinic of Cologne, University of Cologne, Cologne, Germany.
Sarah HuwerDepartment of Obstetrics and Gynecology at the Medical Center, University of Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany.
Benedikt KurzDepartment of Obstetrics and Gynecology at the Medical Center, University of Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany.
Maximilian KlarWomen's Clinic, Cantonal Hospital Aarau, Aarau, Switzerland.
Angeline Favre-InhoferDepartment of Obstetrics and Gynecology at the Medical Center, University of Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany.
Ingolf Juhasz-BössDepartment of Obstetrics and Gynecology at the Medical Center, University of Freiburg, Hugstetterstr. 55, 79106, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe dynamic development towards robotic-assisted surgery particularly affects operative gynecology. The analysis of operative data from robotic-assisted procedures since the first application at a surgical center provides valuable insights into the introduction phase and integration of the DaVinci system into routine clinical operations, as well as their impact on patient care. The aim of this work was to specifically examine the learning curve progression and to present the trend of the professionalization process in implementing the methodology in gynecologic oncology. MATERIALS AND

methodsA retrospective data analysis was conducted of the first n = 107 patients who underwent surgery for a gynecological malignancy with the DaVinci surgical system at the University Medical Center Freiburg between 2020 and 2022. Classic operative parameters were evaluated, including preparation time, skin-to-skin time, console time, and the resulting learning curves of the surgeons and the operative team (including CUSUM analysis and linear regression models). Additionally, perioperative patient characteristics were recorded (e.g., blood loss, length of hospitalization, conversion rate).

resultsThe average operative preparation time is 26.11 ± 8.13 min. The maximum value (CUSUM peak) is at approximately 20 performed procedures, indicating that the processes of operative preparation were mastered after this number of operations. The average skin-to-skin time is 172.84 ± 71.68 min (range 43-387 min), whereby after an initial reduction in skin-to-skin time within the first 30 cases, there was a slight increase in the further course with renewed reduction from approximately 65 procedures. The average console time for all tumor entities is 131.98 ± 63.74 min; for the most common operative indication (endometrial cancer, n = 61), it is 109.89 ± 52.04 min (range 48-221 min). In the surgeons' learning curves, the two surgeons with the most procedures show a CUSUM peak after 11 and 22 procedures, respectively. The average length of stay is 5.00 days (± 2.30). A total of two conversions occurred (conversion rate = 1.9%). DISCUSSION: Upon evaluation of the first n = 107 DaVinci operations, rapid learnability of robotic-assisted operations was demonstrated. The conversion rate was low at 1.9%. A positive effect on the learning curve of individual surgeons was evident after approximately 20 procedures. Both the preparation time and the skin-to-skin time could be rapidly reduced, so that integration into routine clinical operations was possible without problems.

Indexed as

Genital Neoplasms, FemaleGynecologic Surgical ProceduresLearning CurveRobotic Surgical ProceduresAdultAgedFemaleHumansLength of StayMiddle AgedOperative TimeRetrospective StudiesTertiary Care CentersDaVinciGynecologic oncologyLearning curveRobotic-assisted surgerySafety

Identifiers

PMID41667699
PMCPMC12891032

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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.