Evidence map›Paper›PMID 42343032›Full record

ArticleGeneral thoracic and cardiovascular surgery2026

A practical approach to procedural planning for robot-assisted segmentectomy using a lobectomy-based surgical simulator.

Keiji Yamanashi, Ryo Miyata, Takeshi Kawaguchi, Masatsugu Hamaji

Abstract readCase Reports
In one paragraph

Article in General thoracic and cardiovascular surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Keiji YamanashiDepartment of Thoracic and Cardiovascular Surgery, Nara Medical University School of Medicine, 840 Shijo-cho, Kashihara, Nara, 634-8522, Japan. jiike6180@kuhp.kyoto-u.ac.jp.ORCID http://orcid.org/0000-0002-3437-5981
Ryo MiyataDepartment of Thoracic and Cardiovascular Surgery, Nara Medical University School of Medicine, 840 Shijo-cho, Kashihara, Nara, 634-8522, Japan.
Takeshi KawaguchiDepartment of Thoracic and Cardiovascular Surgery, Nara Medical University School of Medicine, 840 Shijo-cho, Kashihara, Nara, 634-8522, Japan.
Masatsugu HamajiDepartment of Thoracic and Cardiovascular Surgery, Nara Medical University School of Medicine, 840 Shijo-cho, Kashihara, Nara, 634-8522, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preoperative planning for robot-assisted segmentectomy requires accurate understanding of segmental anatomy and the sequence of vascular and bronchial division. Although three-dimensional computed tomography provides detailed anatomical information, it does not allow stepwise prediction of intraoperative anatomical changes. We applied a lobectomy-based da Vinci surgical simulator for preoperative planning of robot-assisted apical-anterior segmentectomy. A 62-year-old woman with a 10-mm pulmonary nodule suspected to be stage IA1 non-small cell lung cancer underwent the procedure. The method focuses on rehearsing the sequence of dissection and anticipating post-division anatomical relationships rather than replicating patient-specific anatomy. Although the procedure can be performed without simulation, this approach may provide additional value by enabling procedural rehearsal and reducing intraoperative uncertainty. The simulated workflow closely corresponded to intraoperative findings, suggesting that this approach may serve as a practical adjunct to conventional imaging in selected cases.

Indexed as

Carcinoma, Non-Small-Cell LungComputer SimulationLung NeoplasmsPneumonectomyRobotic Surgical ProceduresFemaleHumansMiddle AgedTomography, X-Ray Computedda Vinci surgical simulatorRobot-assisted thoracic surgerySegmentectomy

Identifiers

PMID42343032
PMCPMC13633352

What OpenQuestion holds

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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.