Evidence map›Paper›PMID 39718324›Full record

ArticleFacts, views & vision in ObGyn2024

Comparison between learning curves of robot-assisted and laparoscopic surgery in gynaecology: a systematic review.

D Raimondo, A Raffone, D Neola, L de Landsheere, R A de Leeuw, L Mereu, T Badotti, E Pazzaglia, R Seracchioli, G Scambia and 1 more

Abstract read
In one paragraph

Article in Facts, views & vision in ObGyn, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
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

11 authors.

D Raimondo
A Raffone
D Neola
L de Landsheere
R A de Leeuw
L Mereu
T Badotti
E Pazzaglia
R Seracchioli
G Scambia
F Fanfani

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The advantages and disadvantages of Robotic Laparoscopic Surgery (RLS) compared to other minimally invasive surgical approaches are debated in the literature. Objective: To evaluate the learning curves (LC) and their assessment methods for Robotic Laparoscopic Surgery (RLS) and Laparoscopic Surgery (LPS) in gynaecologic procedures. Materials and Methods: A systematic review of the literature was performed including the English language observational or interventional studies reporting the absolute number of procedures needed to achieve competency in RLS and LPS gynaecologic procedures, along with an objective and reproducible LC assessment method. Main outcome measures: Number of procedures needed to achieve competency in RLS and LPS and LC assessment methods were extracted from included studies. Results: Six studies with a total of 545 women were included. Several surgical procedures and methods for LC assessment were assessed in the included studies. For radical hysterectomy, bilateral salpingo-oophorectomy and lymph node dissection, the minimum number of procedures required to reach the LC was smaller in RLS than LPS in two studies out of four. For sacrocolpopexy, the number of procedures required to reach the LC was lower in RLS and LPS in one study out of two. Conclusion: RLS learning curve was reported to be quicker than that of LPS for radical hysterectomy, bilateral salpingo-oophorectomy and lymph node dissection. However, a standardised and widely accepted method for LC assessment in endoscopic surgery is needed, as well as further randomised clinical trials, especially involving inexperienced surgeons. What is new?: This study may be the first systematic review to evaluate the LCs and their assessment methods for RLS and LPS in gynaecologic procedures.

Identifiers

PMID39718324
PMCPMC11819794

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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.