Evidence map›Paper›PMID 41741776›Full record

ArticleJournal of robotic surgery2026

Learning curve for robot-assisted rectal resection using the hinotori™ surgical robot system: a risk-adjusted cumulative sum analysis in a surgical team without prior robotic surgery experience.

Akiyoshi Ikebata, Koji Okabayashi, Kohei Shigeta, Hiroyuki Hazama, Masayo Ogiri, Jae-Hoon Yoo, Yumi Egashira, Ryusuke Amemiya, Shinichi Tsuwano, Shigeo Hayatsu

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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 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. Review
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.

Akiyoshi IkebataDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan. akiyoshi1231@gmail.com.ORCID http://orcid.org/0000-0001-6092-1264
Koji OkabayashiDepartment of Surgery, Keio University School of Medicine, Tokyo, Japan.
Kohei ShigetaDepartment of Surgery, Keio University School of Medicine, Tokyo, Japan.
Hiroyuki HazamaDepartment of Gastrointestinal Surgery, Shizuoka General Hospital, Shizuoka, Japan.
Masayo OgiriDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan.
Jae-Hoon YooDepartment of Surgery, Nippon Koukan Hospital, Kawasaki, Japan.
Yumi EgashiraDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan.
Ryusuke AmemiyaDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan.
Shinichi TsuwanoDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan.
Shigeo HayatsuDepartment of Surgery, National Hospital Organization Saitama Hospital, 2-1 Suwa, Wako-shi, Saitama, 351-0102, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Learning curves for robot-assisted rectal resection are mainly reported for the da Vinci platform. We evaluated the learning curve and short-term outcomes after introducing the hinotori™ Surgical Robot System as the first robotic platform for a surgeon with no prior console experience and a team with no prior robotic surgery experience. We retrospectively analyzed 60 consecutive robot-assisted rectal resections performed using the hinotori™ Surgical Robot System at a single center. The primary and secondary outcomes were the console time and robot roll-in–to–console start time, respectively. The case mix was adjusted using the Veenhof technical difficulty score and procedure type, and the change point was identified using risk-adjusted cumulative sum (RA-CUSUM) analysis. The RA-CUSUM curve peaked at case 27, defining the learning (cases 1–27) and stable (cases 28–60) phases. The median console time decreased from 198 min (interquartile range, 137–251) to 156 min (112–193) (p = 0.014) despite increased technical difficulty (median Veenhof score, 12 [9–16] vs. 17 [11–22]; p = 0.040). The median robot roll-in–to–console start time was 9 (8–12) and 9 (7–11) min in the learning and stable phases, respectively (p = 0.817). No surgery was converted to open surgery. In addition, postoperative and key pathological outcomes were comparable between phases. In this first robotic platform setting, our case-mix–adjusted analysis suggested that the console time stabilized after 27 cases. The robot roll-in–to–console start time remained stable throughout, suggesting early standardization of the pre-console workflow.

Indexed as

Learning CurveRectumRobotic Surgical ProceduresAgedFemaleHumansMaleMiddle AgedOperative TimeRetrospective StudiesTime FactorsHinotoriLearning curveRectal cancerRisk adjustmentRobotic surgery

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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.