Evidence map›Paper›PMID 38610613›Full record

ReviewJournal of clinical medicine2024

Innovations to Improve Lung Isolation Training for Thoracic Anesthesia: A Narrative Review.

Corinne Grandjean, Gabriele Casso, Leslie Noirez, Manuel Granell Gil, Georges L Savoldelli, Patrick Schoettker

Open access · goldAbstract readReview
In one paragraph

Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
4.5field-weighted citation impact, top 6% of its field
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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Trial
  2. 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

6 authors at 2 institutions in 2 countries.

Corinne GrandjeanAnesthesiology Department, Hospital of Fribourg, 1708 Fribourg, Switzerland.
Gabriele CassoAnesthesiology and Intensive Care Department, Istituto Cardiocentro EOC, 6900 Lugano, Switzerland.
Leslie NoirezPneumology Department, University Hospital Lausanne, 1005 Lausanne, Switzerland.ORCID 0000-0003-3585-9133
Manuel Granell GilAnesthesiology Department, University of Valencia, 46010 València, Spain.ORCID 0000-0002-8350-3871
Georges L SavoldelliDivision of Anaesthesia, Department of Anaesthesiology, Clinical Pharmacology, Intensive Care and Emergency Medicine, Geneva University Hospitals, Faculty of Medicine, University of Geneva, 1205 Geneva, Switzerland.ORCID 0000-0002-8968-6920
Patrick SchoettkerAnesthesiology Department, University Hospital Lausanne, 1005 Lausanne, Switzerland.ORCID 0000-0001-5370-048X
Universitat de València · ESUniversity of Geneva · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A double-lumen tube or bronchial blocker positioning using flexible bronchoscopy for lung isolation and one-lung ventilation requires specific technical competencies. Training to acquire and retain such skills remains a challenge in thoracic anesthesia. Recent technological and innovative developments in the field of simulation have opened up exciting new horizons and possibilities. In this narrative review, we examine the latest development of existing training modalities while investigating, in particular, the use of emergent techniques such as virtual reality bronchoscopy simulation, virtual airway endoscopy, or the preoperative 3D printing of airways. The goal of this article is, therefore, to summarize the role of existing and future applications of training models/simulators and virtual reality simulators for training flexible bronchoscopy and lung isolation for thoracic anesthesia.

Indexed as

endoscopylung isolationseparationsimulationthoracic anesthesiatrainingvirtual reality

Identifiers

PMID38610613
PMCPMC11012997
OpenAlexW4393142962

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.