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Record W1520923485 · doi:10.1113/jphysiol.2007.150128

The contribution of intrapulmonary shunts to the alveolar‐to‐arterial oxygen difference during exercise is very small

2008· article· en· W1520923485 on OpenAlexaff
Ioannis Vogiatzis, Spyros Zakynthinos, Robert Boushel, Dimitris Athanasopoulos, Jordan A. Guenette, Harrieth Wagner, Charis Roussos, Peter D. Wagner

Bibliographic record

VenueThe Journal of Physiology · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and Diving-Related Complications
Canadian institutionsUniversity of British ColumbiaConcordia University
FundersNational Heart, Lung, and Blood InstituteFoundation for the National Institutes of Health
KeywordsCardiologyMedicineOxygenInternal medicineAnesthesiaChemistry

Abstract

fetched live from OpenAlex

Exercise is well known to cause arterial PO2 to fall and the alveolar-arterial PO2 difference(Aa PO2 ) to increase. Until recently, the physiological basis for this was considered to be mostly ventilation/perfusion ((.)VA/(.)Q) inequality and alveolar-capillary diffusion limitation. Recently, arterio-venous shunting through dilated pulmonary blood vessels has been proposed to explain a significant part of the Aa PO2 during exercise. To test this hypothesis we determined venous admixture during 5 min of near-maximal, constant-load, exercise in hypoxia (in inspired O2 fraction, FIO2 , 0.13), normoxia (FIO2 , 0.21) and hyperoxia (FIO2 , 1.0) undertaken in balanced order on the same day in seven fit cyclists ((.)VO2max, 61.3 +/- 2.4 ml kg(-1) min(-1); mean +/- S.E.M.). Venous admixture reflects three causes of hypoxaemia combined: true shunt, diffusion limitation and ((.)VA/(.)Q) inequality. In hypoxia, venous admixture was 22.8 +/- 2.5% of the cardiac output; in normoxia it was 3.5 +/- 0.5%; in hyperoxia it was 0.5 +/- 0.2%. Since only true shunt accounts for venous admixture while breathing 100% O2, the present study suggests that shunt accounts for only a very small portion of the observed venous admixture, Aa PO2 and hypoxaemia during heavy exercise.

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 score0.970
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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

Citations45
Published2008
Admission routes1
Has abstractyes

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