Oxygen kinetics and debt during recovery from expiratory flow-limited exercise in healthy humans
Bibliographic record
Abstract
In healthy subjects expiratory flow limitation (EFL) during exercise can lower O(2) delivery to the working muscles. We hypothesized that if this affects exercise performance it should influence O(2) kinetics at the end of exercise when the O(2) debt is repaid. We performed an incremental exercise test on six healthy males with a Starling resistor in the expiratory line limiting expiratory flow to approximately 1 l s(-1) to determine maximal EFL exercise workload (W (max)). In two more square-wave exercise runs subjects exercised with and without EFL at W (max) for 6 min, while measuring arterial O(2) saturation (% SaO(2)), end-tidal pressure of CO(2) (P (ET)CO(2)) and breath-by-breath O(2) consumption VO2 taking into account changes in O(2) stored in the lungs. Over the last minute of EFL exercise, mean P (ET)CO(2) (54.7 +/- 9.9 mmHg) was significantly higher (P < 0.05) compared to control (41.4 +/- 3.9 mmHg). At the end of EFL exercise %SaO(2) fell significantly by 4 +/- 3%. When exercise stopped, EFL was removed, and we continued to measure VO2. During recovery, there was an immediate step increase in [Formula: see text] so that repayment of EFL O(2) debt started at a higher VO2 than control. Recovery VO2 kinetics after EFL exercise was best characterized by a double-exponential function with fundamental and slow time constants of 27 +/- 11 and 1,020 +/- 305 s, compared to control values of 41 +/- 10 and 1,358 +/- 320 s, respectively. EFL O(2) debt was 52 +/- 22% greater than control (2.19 +/- 0.58 vs. 1.49 +/- 0.38 l). We conclude that EFL exercise increases the O(2) debt and leads to hypoxemia in part due to hypercapnia.
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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 | 0.001 | 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".