Evidence map›Paper›PMID 30267283›Full record

SynthesisSurgical endoscopy2019

A systematic review of the learning curve in robotic surgery: range and heterogeneity.

I Kassite, T Bejan-Angoulvant, H Lardy, A Binet

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 45 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 6 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

45 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  20. A machine learning framework to adjust for learning effects in medical device safety evaluation.Journal of the American Medical Informatics Association : JAMIA · 2025
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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

4 authors.

I KassitePediatric Surgery Department, Gatien de Clocheville Hospital, University Teaching Hospital of Tours, 37000, Tours, France. kcitibti@gmail.com.
T Bejan-AngoulvantPharmacology Department, Bretonneau Hospital, University Teaching Hospital of Tours, 37000, Tours, France.
H LardyPediatric Surgery Department, Gatien de Clocheville Hospital, University Teaching Hospital of Tours, 37000, Tours, France.
A BinetPediatric Surgery Department, Gatien de Clocheville Hospital, University Teaching Hospital of Tours, 37000, Tours, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the rapid adoption of the robotic surgery, more and more learning curve (LC) papers are being published but there is no set definition of what should constitute a rigorous analysis and represent a true LC. A systematic review of the robotic surgical literature was undertaken to determine the range and heterogeneity of parameters reported in studies assessing the LC in robotic surgery.

methodsThe search was conducted in July 2017 in PubMed. All studies reporting a LC in robotic surgery were included. 268 (25%) of the identified studies met the inclusion criteria.

results102 (38%) studies did not define nor explicitly state the LC with appropriate evidence; 166 studies were considered for quantitative analysis. 46 different parameters of 6 different outcome domains were reported with a median of two parameters (1-8) and 1 domain (1-5) per study. Overall, three domains were only technical and three domains were both technical and clinical/patient-centered outcomes. The two most commonly reported domains were operative time [146 studies (88%)] and intraoperative outcomes [31 studies (19%)]. Postoperative outcomes [16 studies (9%)] and surgical success [11 studies (7%)] were reported infrequently. Purely technical outcomes were the most frequently used to assess LC [131 studies (79%)].

conclusionsThe outcomes reported in studies assessing LC in robotic surgery are extremely heterogeneous and are most often technical indicators of surgical performance rather than clinical and patient-centered outcomes. There is no single outcome that best represents the surgical success. A standardized multi-outcome approach to assessing LC is recommended.

Indexed as

Clinical CompetenceLearning CurveHumansOperative TimeOutcome Assessment, Health CareRobotic Surgical ProceduresLearning curveProficiencyRobotic surgerySurgical outcomes

Identifiers

PMID30267283

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.