Evidence map›Paper›PMID 41784915›Full record

ArticleSports medicine (Auckland, N.Z.)2026

AI-Driven Analysis of Cardiopulmonary Exercise Tests to Identify Gas Exchange and Ventilatory Thresholds.

Daniel A Keir, Andrea Zignoli, Danilo Iannetta, Felipe Mattioni Maturana, Juan M Murias

Abstract read
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In one paragraph

Article in Sports medicine (Auckland, N.Z.), 2026. 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. 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

5 authors.

Daniel A Keir *School of Kinesiology, The University of Western Ontario, TH-4155, 1151 Richmond Street, London, ON, N6A 3K7, Canada. dkeir@uwo.ca.ORCID http://orcid.org/0000-0002-5656-373X
Andrea Zignoli *Department of Industrial Engineering, University of Trento, Trento, Italy.
Danilo IannettaDepartment of Clinical and Experimental Sciences, The University of Brescia, Brescia, Italy.
Felipe Mattioni Maturana *Sports Medicine Department, University Hospital of Tübingen, Tübingen, Germany.
Juan M Murias *College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar.

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN-2016-03698Natural Sciences and Engineering Research Council of Canada RGPIN-2021-03980
6 · The paper itself

Abstract

backgroundA cardiopulmonary exercise test (CPET) provides the estimated lactate threshold (θ

methodsEvaluation included three phases: In phase I, 50 simulated ventilatory and gas exchange CPET files were generated, mixed with 50 authentic files, presented sequentially and in randomized order to three independent evaluators, and judged to be real or fake. In phase II, a new set of 50 files were generated, θ

resultsExperts correctly discriminated simulated from authentic data in 44% of cases (phase I). One-way ANOVA revealed no main effect of identified

conclusionsOxynet can be used as an accurate, reliable, and objective tool to identify or aid in the identification of exercise thresholds from gas exchange and ventilatory CPET data in healthy individuals.

Indexed as

Anaerobic ThresholdArtificial IntelligenceDeep LearningExercise TestPulmonary Gas ExchangeHumansLactic AcidOxygen ConsumptionLactic Acid

Identifiers

PMID41784915

What OpenQuestion holds

Textmetadata
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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.