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Record W2526977019 · doi:10.2147/copd.s112473

An in silico analysis of oxygen uptake of a mild COPD patient during rest and exercise using a portable oxygen concentrator

2016· article· en· W2526977019 on OpenAlexaff
Ira Katz, Marine Pichelin, Spyridon Montesantos, Min‐Yeong Kang, B. Sapoval, Kaixian Zhu, Charles-Philippe Thévenin, R. W. McCoy, Andrew R. Martin, Georges Caillibotte

Bibliographic record

VenueInternational Journal of COPD · 2016
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOxygen pulseBreathingPulse (music)OxygenOxygenationMedicineBiomedical engineeringSimulationComputer scienceAnesthesiaChemistryVO2 maxHeart rateInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

Oxygen treatment based on intermittent-flow devices with pulse delivery modes available from portable oxygen concentrators (POCs) depends on the characteristics of the delivered pulse such as volume, pulse width (the time of the pulse to be delivered), and pulse delay (the time for the pulse to be initiated from the start of inhalation) as well as a patient's breathing characteristics, disease state, and respiratory morphology. This article presents a physiological-based analysis of the performance, in terms of blood oxygenation, of a commercial POC at different settings using an in silico model of a COPD patient at rest and during exercise. The analysis encompasses experimental measurements of pulse volume, width, and time delay of the POC at three different settings and two breathing rates related to rest and exercise. These experimental data of device performance are inputs to a physiological-based model of oxygen uptake that takes into account the real dynamic nature of gas exchange to illustrate how device- and patient-specific factors can affect patient oxygenation. This type of physiological analysis that considers the true effectiveness of oxygen transfer to the blood, as opposed to delivery to the nose (or mouth), can be instructive in applying therapies and designing new devices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.298
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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