Evidence map›Paper›PMID 42604282›Full record

ArticleJTCVS open2026

Learning curve analysis of robotic-assisted thoracoscopic lung resection performed by a novice thoracic surgeon.

Taiki Takasugi, Tohru Mawatari

Abstract read
In one paragraph

Article in JTCVS open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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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

2 authors.

Taiki TakasugiDepartment of Thoracic Surgery, Hakodate Municipal Hospital, Hakodate, Japan.
Tohru MawatariDepartment of Thoracic Surgery, Hakodate Municipal Hospital, Hakodate, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Robot-assisted thoracoscopic surgery has been increasingly adopted for lung resection, and opportunities for novice surgeons to perform this procedure are expanding. However, evidence regarding the early learning curves among novice thoracic surgeons remains limited. We performed this study to evaluate the learning curve and perioperative outcomes of robot-assisted thoracoscopic surgery for anatomical lung resections performed by novice surgeons. Methods: We reviewed 30 consecutive patients who underwent robot-assisted thoracoscopic anatomical lung resections between April and December 2025. All procedures were performed by a single novice thoracic surgeon with 4 years of experience in thoracic surgery. The primary outcome was console duration. Secondary outcomes included operative time, estimated blood loss, conversion to thoracotomy, postoperative complications, air leaks, chest drain duration, and length of hospital stay. Learning curve analyses were performed using scatter plots, moving averages, and cumulative sum analyses. Results: The median console time was 122 minutes (range, 50-221 minutes), which decreased with increasing experience. The median operative time was 186 minutes (range, 97-306 minutes) and showed a similar downward trend. Cumulative sum analysis identified a clear point indicating procedural proficiency after approximately 20 cases. The median blood loss was 20 mL, which was consistently low. One planned conversion to thoracotomy (3.3%) was performed because of severe hilar adhesions. No major postoperative complications (Clavien-Dindo grade ≥III) were observed. Conclusions: Robot-assisted thoracoscopic anatomical lung resection can be safely performed by novice thoracic surgeons using a structured training framework. Procedural proficiency was achieved in approximately 20 cases, with favorable perioperative outcomes.

Indexed as

learning curvelung cancernovice surgeonrobotic-assisted thoracic surgery

Identifiers

PMID42604282
PMCPMC13477050

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.