Evidence map›Paper›PMID 31376007›Full record

SynthesisSurgical endoscopy2019

Defining the learning curve of robotic thoracic surgery: what does it take?

Alexandra D Power, Desmond M D'Souza, Susan D Moffatt-Bruce, Robert E Merritt, Peter J Kneuertz

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Surgical endoscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Observational
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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

5 authors.

Alexandra D PowerDivision of Thoracic Surgery, Department of Surgery, The Ohio State University Wexner Medical Center, 410 West 10th Avenue; N846a Doan Hall, Columbus, OH, 43210, USA.
Desmond M D'SouzaDivision of Thoracic Surgery, Department of Surgery, The Ohio State University Wexner Medical Center, 410 West 10th Avenue; N846a Doan Hall, Columbus, OH, 43210, USA.
Susan D Moffatt-BruceDivision of Thoracic Surgery, Department of Surgery, The Ohio State University Wexner Medical Center, 410 West 10th Avenue; N846a Doan Hall, Columbus, OH, 43210, USA.
Robert E MerrittDivision of Thoracic Surgery, Department of Surgery, The Ohio State University Wexner Medical Center, 410 West 10th Avenue; N846a Doan Hall, Columbus, OH, 43210, USA.
Peter J KneuertzDivision of Thoracic Surgery, Department of Surgery, The Ohio State University Wexner Medical Center, 410 West 10th Avenue; N846a Doan Hall, Columbus, OH, 43210, USA. Peter.Kneuertz@osumc.edu.ORCID 0000-0002-3042-6120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundControversy exists as to what constitutes a learning curve to achieve competency, and how the initial learning period of robotic thoracic surgery should be approached.

methodsWe conducted a systematic review of the literature published prior to December 2018 using PubMed/MEDLINE for studies of surgeons adopting the robotic approach for anatomic lung resection or thymectomy. Changes in operating room time and outcomes based on number of cases performed, type of procedure, and existing proficiency with video-assisted thoracoscopic surgery (VATS) were examined.

resultsTwelve observational studies were analyzed, including nine studies on robotic lung resection and three studies on thymectomy. All studies showed a reduction in operative time with an increasing number of cases performed. A steep learning curve was described for thymectomy, with a decrease in operating room time in the first 15 cases and a plateau after 15-20 cases. For anatomic lung resection, the number of cases to achieve a plateau in operative time ranged between 15-20 cases and 40-60 cases. All but two studies had at least some VATS experience. Six studies reported on experience of over one hundred cases and showed continued gradual improvements in operating room time.

conclusionThe learning curve for robotic thoracic surgery appears to be rapid with most studies indicating the steepest improvement in operating time occurring in the initial 15-20 cases for thymectomy and 20-40 cases for anatomic lung resection. Existing data can guide a standardized robotic curriculum for rapid adaptation, and aid credentialing and quality monitoring for robotic thoracic surgery programs.

Indexed as

Learning CurveHumansOperative TimePneumonectomyRobotic Surgical ProceduresThymectomyLearning curveLobectomyRobotic surgerySegmentectomyThymectomy

Identifiers

PMID31376007

What OpenQuestion holds

Textmetadata
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.