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Oxygen Uptake Kinetics Are Slowed in Cystic Fibrosis

2005· article· en· W2055132110 on OpenAlexaff
Helge Hebestreit, Alexandra Hebestreit, A. Trusen, Richard L. Hughson

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2005
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsOxygenCystic fibrosisCyclingKineticsOxygen saturationIntensity (physics)Heart rateInternal medicineCardiologyMedicineChemistryBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: There are conflicting reports on the kinetics of oxygen uptake at the onset of exercise in patients with cystic fibrosis (CF). The objective of the present study was, therefore, to compare oxygen uptake (VO(2) kinetics in patients with CF with those of healthy controls (CON). METHODS: Eighteen CF patients (FEV1 37-98% predicted) and 15 CON aged 10-33 yr completed two to four transitions from low-intensity cycling (stage 1, 20 W) to cycling at 1.3-1.4 W.kg(-1) body weight (stage 2). There was no difference between groups in heart rate at stages 1 and 2 or in relative exercise intensity, as expressed as percent VO(2peak) or percentage of ventilatory threshold. However, oxygen saturation (SpO(2)) was lower in the patients with CF during both stages. VO(2) data were interpolated second by second, time-aligned, and averaged. Monoexponential equations were used to describe phase II VO(2) responses. RESULTS: Although there were no differences between CF and CON in amplitude (10.9 +/- 1.8 vs 10.2 +/- 1.6 mL O2.W(-1)) of phase II VO(2) response, the time constant tau was significantly prolonged in CF compared with CON (36.8 +/- 13.6 vs 26.4 +/- 9.1 s). When tau was adjusted for the effects of FEV1 or SpO(2) during submaximal exercise, the difference between CF patients and controls disappeared. CONCLUSION: VO(2) kinetics are slowed in CF, which may, in part, be attributed to an impairment of oxygen delivery.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.308
Teacher spread0.291 · 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.

Study designObservational
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

Citations56
Published2005
Admission routes1
Has abstractyes

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