Evidence map›Paper›PMID 36910116›Full record

ReviewJournal of thoracic disease2023

Suggested robotic-assisted thoracic surgery training curriculum.

Muteb Al Zaidi, Gavin M Wright, Kazuhiro Yasufuku

Abstract readReview
In one paragraph

Review in Journal of thoracic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
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

3 authors.

Muteb Al ZaidiDivision of Thoracic Surgery, University Health Network, Toronto General Hospital, Toronto, ON, Canada.
Gavin M WrightSt. Vincent's Hospital Melbourne, Melbourne, VIC, Australia.
Kazuhiro YasufukuDivision of Thoracic Surgery, University Health Network, Toronto General Hospital, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic-assisted surgery, a technological advancement in the field of surgery, has become increasingly popular among surgeons of many specialties over time. Robotic-assisted thoracic surgery (RATS) is comparable to video-assisted thoracic surgery (VATS) in terms of patient care outcomes; however, the perception of increased operative time and a lack of cost-effectiveness have led to controversy regarding its alleged benefits. Nevertheless, robotic surgery is one of the preferred options for minimally invasive surgery by some thoracic surgeon over VATS, due to its ability to provide 3-D vision, precise wrist movements, enhanced magnification, and instrument stability and articulation. Notably, trainees in the field of thoracic surgery experience difficulty gaining knowledge and learning skills associated with RATS due to its complexity, limited access to robotic instruments, the lack of a standardized curriculum for trainees, and lack of mentorship or proctorship, thus leading to a steeper learning curve compared to laparoscopic or VATS procedures that are cost-friendly, easy to learn, and feasible to practice. Nevertheless, focusing on RATS training for thoracic surgeons will keep them familiar with robotic techniques, including the pre-operative setup and intra-operative process, which will ultimately decrease operative times. In this paper, we will review the literature, express and discuss the most viable training curriculum from authors' point of view to help achieve this goal.

Indexed as

Robotic-assisted thoracic surgery (RATS)robotic surgery training curriculumrobotic thoracic surgery training program

Identifiers

PMID36910116
PMCPMC9992622

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.