Evidence map›Paper›PMID 41521378›Full record

ArticleJournal of robotic surgery2026

Learning curves and outcomes of robotic colorectal surgery: A single-surgeon experience within a structured dual-console training program.

Zsolt Madarasz, Krysztof Nowakowski, Michael Leitz, Bogdan-Cornel Sturzu, Anas Baltamar, Kira Baginski, Annika Hoyer, Miljana Vladimirov, Jens Hoeppner, Fabian Nimczewski

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In one paragraph

Article in Journal of robotic surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. 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.

Zsolt MadaraszDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany. zsolt.madarasz@klinikum-lippe.de.
Krysztof NowakowskiDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Michael LeitzDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Bogdan-Cornel SturzuDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Anas BaltamarDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Kira BaginskiBiostatistics and Medical Biometry, Medical School OWL, Bielefeld University, 33615, Bielefeld, Germany.
Annika HoyerBiostatistics and Medical Biometry, Medical School OWL, Bielefeld University, 33615, Bielefeld, Germany.
Miljana VladimirovDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Jens HoeppnerDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.
Fabian NimczewskiDepartment of Surgery, Medical School, Bielefeld University, University Medical Center OWL, Campus Hospital Lippe, 32756, Detmold, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic colorectal surgery has gained broad acceptance, but defining the learning curve and safety profile during structured implementation remains essential. This single-surgeon study aimed to analyze procedural proficiency and oncological outcomes during the introduction of robotic colorectal surgery within a dual-console training framework. All robotic colorectal resections performed between September 2021, and December 2024 were retrospectively analyzed. Procedures included robotic anterior resection (R-AR), robotic low anterior resection (R-LAR), and robotic right colectomy (R-RC). Cumulative sum (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) analyses were used to evaluate operative performance and safety. Demographic, perioperative, and histopathological parameters were analyzed descriptively. A total of 102 procedures were performed by the surgeon as the primary console operator (R-AR = 46, R-LAR = 32, R-RC = 24). Mean operative times were 163 ± 44 min (R-AR), 228 ± 56 min (R-LAR), and 154 ± 25 min (R-RC), respectively. Distinct learning curve turning points were observed for two procedures, with proficiency reached after approximately 28 R-AR cases and 16 R-RC cases. In contrast, R-LAR did not show a clear turning point but demonstrated a prolonged plateau between cases 6 and 27. Conversion rate was 1%, major complications (≥ Clavien–Dindo IIIb) occurred in 4.9%, and there were no intraoperative adverse events. The R0 resection rate exceeded 97% (R-AR: 100%, R-LAR: 96.9%, R-RC: 95.8%), and mean lymph-node yield was 28.2 ± 13. CUSUM and RA-CUSUM curves confirmed stable performance and consistent oncological quality throughout the learning phase. Robotic colorectal surgery can be safely implemented in academic centers within a structured dual-console training environment. Procedural proficiency was achieved after 28 rectal resections and 16 right colectomies.

Indexed as

Clinical CompetenceLearning CurveRobotic Surgical ProceduresSurgeonsAgedColectomyColorectal Surgical ProceduresFemaleHumansMaleMiddle AgedOperative TimeRetrospective StudiesTreatment OutcomeCUSUMDual consoleLearning curveOncological outcomesRA-CUSUMRobotic colorectal surgerySingle surgeonTraining program

Identifiers

PMID41521378
PMCPMC12791061

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.