Evidence map›Paper›PMID 42022840›Full record

ArticleFluids (Basel, Switzerland)2025

Geometric Analyses of the Expiratory Flow-Volume Curve to Identify Expiratory Flow Limitation During Exercise.

Hans Haverkamp, Gregory Petrics, Yannick Molgat-Seon

Abstract read
In one paragraph

Article in Fluids (Basel, Switzerland), 2025. 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
–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

0 citing papers in PubMed.

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

3 authors.

Hans HaverkampDepartment of Nutrition and Exercise Physiology, Elson S. Floyd College of Medicine, Washington State University, 412 E. Spokane Falls Blvd., Spokane, WA 99202, USA.
Gregory PetricsDepartment of Mathematics, Vermont State University, Johnson, VT 05656, USA.
Yannick Molgat-SeonDepartment of Kinesiology and Applied Health, University of Winnipeg, Winnipeg, MB R3B 2E9, Canada.

Funding

Vermont INBRE Administrative Supplement 2024 AWD 118P20GM103449 · NIGMS · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI Julie Dragon · 2012 to 2026
$58.4M
NIGMS NIH HHS P20 GM103449
6 · The paper itself

Abstract

An important purpose of cardiopulmonary exercise testing (CPET) is to query the mechanisms for unexplained shortness of breath or exaggerated exertional dyspnea. Expiratory flow limitation (EFL) is an important indicator of ventilatory constraint that can negatively influence both dyspnea and exercise capacity. Unfortunately, due to logistical challenges and lack of sufficient clinical training, EFL is rarely measured during CPET. The conventional method for identifying exercise EFL is limited because it requires patient cooperation and it is also dependent on the maximal expiratory flow-volume curve, which underestimates actual maximal expiratory flow during exercise. Simplified methods for identifying EFL that are based on the shape of the exercise tidal flow-volume curve would improve the accessibility of measuring EFL during exercise. The overall aim of this review is to critically review the approaches and methods used to measure EFL in exercising adults. We review the physiology underlying EFL and the conventional methods for determining exercise EFL. We then provide critical analyses of more recent methods for identifying exercise EFL that are based on the geometry of the exercise tidal expiratory flow-volume curve. Finally, we highlight recent work designed to assess exercise EFL using a type of deep machine learning known as a convolutional neural network.

Indexed as

breathing mechanicscardiopulmonary exercise testingdeep learningdyspneaexerciseexertional dyspneaventilatory constraint

Identifiers

PMID42022840
PMCPMC13099237

What OpenQuestion holds

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Registered trials

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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.