Evidence map›Paper›PMID 38475015›Full record

ArticleSensors (Basel, Switzerland)2024

A New Model of Air-Oxygen Blender for Mechanical Ventilators Using Dynamic Pressure Sensors.

Gabryel F Soares, Gilberto Fernandes, Otacílio M Almeida, Gildario D Lima, Joel J P C Rodrigues

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 3 institutions in 2 countries.

Gabryel F SoaresDepartment of Electrical Engineering, Federal University of Piauí (UFPI), Teresina 64049-550, Brazil.
Gilberto FernandesComputer Science Department, State University of Londrina (UEL), Londrina 86057-970, Brazil.ORCID 0000-0002-4498-2427
Otacílio M AlmeidaDepartment of Electrical Engineering, Federal University of Piauí (UFPI), Teresina 64049-550, Brazil.ORCID 0000-0001-6950-2891
Gildario D LimaFederal University of Delta do Parnaíba (UFDPar), Parnaíba 64202-020, Brazil.
Joel J P C RodriguesCOPELABS, Lusófona University, 1749-024 Lisbon, Portugal.ORCID 0000-0001-8657-3800
Universidade Federal do Piauí · BRUniversidade Estadual de Londrina · BRUniversidade Lusófona · PT

Funding

National Council for Scientific and Technological Development Grant No. 306607/2023-9
6 · The paper itself

Abstract

Respiratory diseases are among the leading causes of death globally, with the COVID-19 pandemic serving as a prominent example. Issues such as infections affect a large population and, depending on the mode of transmission, can rapidly spread worldwide, impacting thousands of individuals. These diseases manifest in mild and severe forms, with severely affected patients requiring ventilatory support. The air-oxygen blender is a critical component of mechanical ventilators, responsible for mixing air and oxygen in precise proportions to ensure a constant supply. The most commonly used version of this equipment is the analog model, which faces several challenges. These include a lack of precision in adjustments and the inspiratory fraction of oxygen, as well as gas wastage from cylinders as pressure decreases. The research proposes a blender model utilizing only dynamic pressure sensors to calculate oxygen saturation, based on Bernoulli's equation. The model underwent validation through simulation, revealing a linear relationship between pressures and oxygen saturation up to a mixture outlet pressure of 500 cmH

Indexed as

OxygenPandemicsCalibrationHumansPressureVentilators, MechanicalOxygenblenderFIO2mechanical ventilatorsimulation

Identifiers

PMID38475015
PMCPMC10933838
OpenAlexW4392157157

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.