Evidence map›Paper›PMID 28411345›Full record

ReviewSurgical endoscopy2017

An appraisal of the learning curve in robotic general surgery.

Luise I M Pernar, Faith C Robertson, Ali Tavakkoli, Eric G Sheu, David C Brooks, Douglas S Smink

Abstract readReview
PubMed Publisher
In one paragraph

Review in Surgical endoscopy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 7 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

85 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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25 more citing papers are in PubMed but not listed here.

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

6 authors.

Luise I M PernarDepartment of Surgery, Boston Medical Center, 88 E. Newton Street, Boston, MA, 02118, USA.
Faith C RobertsonHarvard Medical School, Boston, MA, USA.
Ali TavakkoliDepartment of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Eric G SheuDepartment of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
David C BrooksDepartment of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Douglas S SminkDepartment of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA. dsmink@partners.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRobotic-assisted surgery is used with increasing frequency in general surgery for a variety of applications. In spite of this increase in usage, the learning curve is not yet defined. This study reviews the literature on the learning curve in robotic general surgery to inform adopters of the technology.

methodsPubMed and EMBASE searches yielded 3690 abstracts published between July 1986 and March 2016. The abstracts were evaluated based on the following inclusion criteria: written in English, reporting original work, focus on general surgery operations, and with explicit statistical methods.

resultsTwenty-six full-length articles were included in final analysis. The articles described the learning curves in colorectal (9 articles, 35%), foregut/bariatric (8, 31%), biliary (5, 19%), and solid organ (4, 15%) surgery. Eighteen of 26 (69%) articles report single-surgeon experiences. Time was used as a measure of the learning curve in all studies (100%); outcomes were examined in 10 (38%). In 12 studies (46%), the authors identified three phases of the learning curve. Numbers of cases needed to achieve plateau performance were wide-ranging but overlapping for different kinds of operations: 19-128 cases for colorectal, 8-95 for foregut/bariatric, 20-48 for biliary, and 10-80 for solid organ surgery.

conclusionAlthough robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.

Indexed as

Learning CurveClinical CompetenceGeneral SurgeryHumansRobotic Surgical ProceduresSurgeonsCase volumeLearning curveMinimally invasive surgeryRobotSurgical procedure

Identifiers

PMID28411345

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.