Evidence map›Paper›PMID 35336353›Full record

ReviewSensors (Basel, Switzerland)2022

Air-Oxygen Blenders for Mechanical Ventilators: A Literature Review.

Gabryel F Soares, Otacílio M Almeida, José W M Menezes, Sergei S A Kozlov, Joel J P C Rodrigues

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Gabryel F SoaresDepartment of Electrical Engineering, Universidade Federal do Piauí, Teresina 64049-550, Brazil.
Otacílio M AlmeidaDepartment of Electrical Engineering, Universidade Federal do Piauí, Teresina 64049-550, Brazil.
José W M MenezesDepartament of Telematics, Federal Institute of Ceará, Fortaleza 60040-531, Brazil.
Sergei S A KozlovPhotonics and Optoinformatics Faculty, ITMO University, 191002 St. Petersburg, Russia.ORCID 0000-0002-2692-627X
Joel J P C RodriguesPhotonics and Optoinformatics Faculty, ITMO University, 191002 St. Petersburg, Russia.ORCID 0000-0001-8657-3800

Funding

Fundação para a Ciência e Tecnologia Project UIDB/50008/2020Ministry of and Higher Education of the Russian Federation Grant 08-08National Council for Scientific and Technological Development Grant No. 313036/2020-9
6 · The paper itself

Abstract

Respiratory diseases are one of the most common causes of death in the world and this recent COVID-19 pandemic is a key example. Problems such as infections, in general, affect many people and depending on the form of transmission they can spread throughout the world and weaken thousands of people. Two examples are severe acute respiratory syndrome and the recent coronavirus disease. These diseases have mild and severe forms, in which patients gravely affected need ventilatory support. The equipment that serves as a basis for operation of the mechanical ventilator is the air-oxygen blender, responsible for carrying out the air-oxygen mixture in the proper proportions ensuring constant supply. New blender models are described in the literature together with applications of control techniques, such as Proportional, Integrative and Derivative (PID); Fuzzy; and Adaptive. The results obtained from the literature show a significant improvement in patient care when using automatic controls instead of manual adjustment, increasing the safety and accuracy of the treatment. This study presents a deep review of the state of the art in air-oxygen benders, identifies the most relevant characteristics, performs a comparison study considering the most relevant available solutions, and identifies open research directions in the topic.

Indexed as

COVID-19OxygenHumansPandemicsVentilators, MechanicalOxygenblendercontrolCOVID-19fraction of inspired oxygenInternet of Things (IoT)oxygen saturationrespiratory diseases

Identifiers

PMID35336353
PMCPMC8954851

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.