Evidence map›Paper›PMID 40053194›Full record

ArticleTechniques in coloproctology2025

Learning curve analysis for prophylactic bilateral robot-assisted lateral lymph node dissection for lower rectal cancer: a retrospective study.

T Sueda, M Yasui, J Nishimura, Y Kagawa, M Kitakaze, R Mori, C Matsuda, Y Ushimaru, T Sugase, Y Mukai and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Techniques in coloproctology, 2025. 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

18 authors.

T SuedaDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan. sueda811@yahoo.co.jp.
M YasuiDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
J NishimuraDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
Y KagawaDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
M KitakazeDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
R MoriDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
C MatsudaDepartment of Surgery, Osaka Police Hospital, 10-31 Kitayamacho-tennojiku, Osaka, 543-0035, Japan.
Y UshimaruDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
T SugaseDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
Y MukaiDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
H KomatsuDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
Y YanagimotoDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
T KanemuraDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
K YamamotoDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
H WadaDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
K GotoDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
H MiyataDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.
M OhueDepartment of Gastroenterological Surgery, Osaka International Cancer Institute, 69-1-3 Ootemae-Chuoku, Osaka, Osaka, 540-0008, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLateral lymph node dissection (LLND) is an important surgical procedure in the treatment of lower rectal cancer (RC). However, limited data are available regarding the learning curve for robot-assisted LLND (RA-LLND). This study aimed to evaluate the learning curve for prophylactic bilateral RA-LLND for lower RC.

methodsWe retrospectively analyzed 58 consecutive patients with clinical stage II/III lower RC who had undergone prophylactic bilateral RA-LLND between July 2020 and June 2024. Cumulative sum (CUSUM) analysis was used to evaluate the learning curve for bilateral RA-LLND operative time.

resultsThe mean age of patients was 61.5 years, and mean body mass index was 23.4 kg/m

conclusionsThe three phases identified by CUSUM analysis represented characteristics of the learning curve for prophylactic bilateral RA-LLND. These data suggest that 20 cases are required for the early stage of the learning curve, whereas mastery level could be achieved after 36 cases.

Indexed as

Learning CurveLymph Node ExcisionRectal NeoplasmsRobotic Surgical ProceduresAgedClinical CompetenceFemaleHumansMaleMiddle AgedOperative TimePostoperative ComplicationsRetrospective StudiesTreatment OutcomeCUSUM methodologyLateral pelvic lymph node dissectionLearning curveRectal cancerRobotic surgery

Identifiers

PMID40053194
PMCPMC11889013

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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.