Effects of Hyperoxia on Ventilatory Limitation During Exercise in Advanced Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
We studied interrelationships between exercise endurance, ventilatory demand, operational lung volumes, and dyspnea during acute hyperoxia in ventilatory-limited patients with advanced chronic obstructive pulmonary disease (COPD). Eleven patients with COPD (FEV(1.0) = 31 +/- 3% predicted, mean +/- SEM) and chronic respiratory failure (Pa(O(2)) 52 +/- 2 mm Hg, Pa(CO(2 ))48 +/- 2 mm Hg) breathed room air (RA) or 60% O(2) during two cycle exercise tests at 50% of their maximal exercise capacity, in randomized order. Endurance time (T(lim)), dyspnea intensity (Borg Scale), ventilation (V E), breathing pattern, dynamic inspiratory capacity (IC(dyn)), and gas exchange were compared. Pa(O(2)) at end-exercise was 46 +/- 3 and 245 +/- 10 mm Hg during RA and O(2), respectively. During O(2), T(lim) increased 4.7 +/- 1.4 min (p < 0.001); slopes of Borg, V E, V CO(2), and lactate over time fell (p < 0.05); slopes of Borg-V E, V E-V CO(2), V E-lactate were unchanged. At a standardized time near end-exercise, O(2) reduced dyspnea 2.0 +/- 0.5 Borg units, V CO(2) 0.06 +/- 0.03 L/min, V E 2.8 +/- 1.0 L/min, and breathing frequency 4.4 +/- 1.1 breaths/min (p < 0.05 each). IC(dyn) and inspiratory reserve volume (IRV) increased throughout exercise with O(2) (p < 0.05). Increased IC(dyn) was explained by the combination of increased resting IRV and decreased exercise breathing frequency (r(2) = 0.83, p < 0.0005). In conclusion, improved exercise endurance during hyperoxia was explained, in part, by a combination of reduced ventilatory demand, improved operational lung volumes, and dyspnea alleviation.
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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 | 0.002 |
| 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) | 0.000 | 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".