Determinants of maximal oxygen uptake in severe acute hypoxia
Bibliographic record
Abstract
To unravel the mechanisms by which maximal oxygen uptake (VO2 max) is reduced with severe acute hypoxia in humans, nine Danish lowlanders performed incremental cycle ergometer exercise to exhaustion, while breathing room air (normoxia) or 10.5% O2 in N2 (hypoxia, approximately 5,300 m above sea level). With hypoxia, exercise PaO2 dropped to 31-34 mmHg and arterial O2 content (CaO2) was reduced by 35% (P < 0.001). Forty-one percent of the reduction in CaO2 was explained by the lower inspired O2 pressure (PiO2) in hypoxia, whereas the rest was due to the impairment of the pulmonary gas exchange, as reflected by the higher alveolar-arterial O2 difference in hypoxia (P < 0.05). Hypoxia caused a 47% decrease in VO2 max (a greater fall than accountable by reduced CaO2). Peak cardiac output decreased by 17% (P < 0.01), due to equal reductions in both peak heart rate and stroke VOlume (P < 0.05). Peak leg blood flow was also lower (by 22%, P < 0.01). Consequently, systemic and leg O2 delivery were reduced by 43 and 47%, respectively, with hypoxia (P < 0.001) correlating closely with VO2 max (r = 0.98, P < 0.001). Therefore, three main mechanisms account for the reduction of VO2 max in severe acute hypoxia: 1) reduction of PiO2, 2) impairment of pulmonary gas exchange, and 3) reduction of maximal cardiac output and peak leg blood flow, each explaining about one-third of the loss in VO2 max.
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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 | 0.001 |
| 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".