Comparison of Respiratory and Circulatory Responses to 1-Leg, 2-Leg Knee Extension Exercise and Cycling in Patients with COPD
Bibliographic record
Abstract
Background: The mass-specific work rates at which peripheral circulatory blood flow (BF) may become compromised in COPD due to a “respiratory steal” phenomenon was examined using one leg (1-leg KE), two-leg knee extension (2-leg KE) and cycling. Methods: Eleven COPD patients (age: 66 yr ± 8; FEV1= 45% predicted ± 14) did three steady states of cycling, 1-leg KE and 2-leg KE at 20, 40 and 65% of peak power (SS20%; SS40%; SS65%). Ventilation, VO2, arterial blood gas and dye dilution cardiac output were measured at rest and during exercise. Inspiratory capacity (IC) was used to monitor operating lung volume responses and determine dynamic hyperinflation. Results: Preliminary data show higher VO2 (L•min-1) during cycling than 1-leg KE and 2-leg KE (SS65% 1.0 ± 0.2 vs 0.5 ± 0.1 vs 0.6 ± 0.1). Despite the smaller muscle mass involvement of 1-leg KE, breathing frequency during SS65% was not different from that of cycling at SS65% (27 ± 5 vs 28 ± 5). During 1-leg KE, a plateau in tidal volume (0.8L ± 0.1) was achieved at SS20% while during cycling, tidal volume increased up to 1.4L ± 0.3 at SS65%. Similar falls in pH from baseline and similar PCO2 measures were seen with cycling (baseline: pH 7.43 ± 0.02; PaCO2 mmHg 39 ± 2 to SS65%: 7.38 ± 0.03; 40 ± 3) and 1-leg KE (baseline: pH 7.41 ± 0.03; PaCO2 mmHg 39 ± 2 to SS65%: 7.39 ± 0.02; 40 ± 3) although SaO2 was maintained with 1-leg KE, but not with cycling or 2-leg KE (95 to 93% at SS65%). IC (in % total lung capacity) was similarly reduced from baseline in 1-leg KE (30 ± 2 to 23 ± 3) and during cycling (30 ± 2 to 24 ± 3). Conclusion: These data suggest that ventilatory and gas exchange responses depend on the relative symptom-limited exercise intensity and not exercise modality.
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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".