The effect of hypoxia on pulmonary O<sub>2</sub> uptake, leg blood flow and muscle deoxygenation during single‐leg knee‐extension exercise
Bibliographic record
Abstract
The effect of hypoxic breathing on pulmonary O(2) uptake (VO(2p)), leg blood flow (LBF) and O(2) delivery and deoxygenation of the vastus lateralis muscle was examined during constant-load single-leg knee-extension exercise. Seven subjects (24 +/- 4 years; mean +/-s.d.) performed two transitions from unloaded to moderate-intensity exercise (21 W) under normoxic and hypoxic (P(ET)O(2)= 60 mmHg) conditions. Breath-by-breath VO(2p) and beat-by-beat femoral artery mean blood velocity (MBV) were measured by mass spectrometer and volume turbine and Doppler ultrasound (VingMed, CFM 750), respectively. Deoxy-(HHb), oxy-, and total haemoglobin/myoglobin were measured continuously by near-infrared spectroscopy (NIRS; Hamamatsu NIRO-300). VO(2p) data were filtered and averaged to 5 s bins at 20, 40, 60, 120, 180 and 300 s. MBV data were filtered and averaged to 2 s bins (1 contraction cycle). LBF was calculated for each contraction cycle and averaged to 5 s bins at 20, 40, 60, 120, 180 and 300 s. VO(2p) was significantly lower in hypoxia throughout the period of 20, 40, 60 and 120 s of the exercise on-transient. LBF (l min(-1)) was approximately 35% higher (P > 0.05) in hypoxia during the on-transient and steady-state of KE exercise, resulting in a similar leg O(2) delivery in hypoxia and normoxia. Local muscle deoxygenation (HHb) was similar in hypoxia and normoxia. These results suggest that factors other than O(2) delivery, possibly the diffusion of O(2,) were responsible for the lower O(2) uptake during the exercise on-transient in hypoxia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".