Highs and lows of hyperoxia: physiological, performance, and clinical aspects
Bibliographic record
Abstract
Molecular oxygen (O 2 ) is a vital element in human survival and plays a major role in a diverse range of biological and physiological processes. Although normobaric hyperoxia can increase arterial oxygen content ([Formula: see text]), it also causes vasoconstriction and hence reduces O 2 delivery in various vascular beds, including the heart, skeletal muscle, and brain. Thus, a seemingly paradoxical situation exists in which the administration of oxygen may place tissues at increased risk of hypoxic stress. Nevertheless, with various degrees of effectiveness, and not without consequences, supplemental oxygen is used clinically in an attempt to correct tissue hypoxia (e.g., brain ischemia, traumatic brain injury, carbon monoxide poisoning, etc.) and chronic hypoxemia (e.g., severe COPD, etc.) and to help with wound healing, necrosis, or reperfusion injuries (e.g., compromised grafts). Hyperoxia has also been used liberally by athletes in a belief that it offers performance-enhancing benefits; such benefits also extend to hypoxemic patients both at rest and during rehabilitation. This review aims to provide a comprehensive overview of the effects of hyperoxia in humans from the “bench to bedside.” The first section will focus on the basic physiological principles of partial pressure of arterial O 2 , [Formula: see text], and barometric pressure and how these changes lead to variation in regional O 2 delivery. This review provides an overview of the evidence for and against the use of hyperoxia as an aid to enhance physical performance. The final section addresses pathophysiological concepts, clinical studies, and implications for therapy. The potential of O 2 toxicity and future research directions are also considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".