Physiological Correlates of Endurance Time Variability during Constant-Workrate Cycling Exercise in Patients with COPD
Bibliographic record
Abstract
RATIONALE: The endurance time (T(end)) during constant-workrate cycling exercise (CET) is highly variable in COPD. We investigated pulmonary and physiological variables that may contribute to these variations in T(end). METHODS: Ninety-two patients with COPD completed a CET performed at 80% of peak workrate capacity (W(peak)). Patients were divided into tertiles of T(end) [Group 1: <4 min; Group 2: 4-6 min; Group 3: >6 min]. Disease severity (FEV(1)), aerobic fitness (W(peak), peak oxygen consumption [VO2(peak)], ventilatory threshold [VO2(VT)]), quadriceps strength (MVC), symptom scores at the end of CET and exercise intensity during CET (heart rate at the end of CET to heart rate at peak incremental exercise ratio [HR(CET)/HR(peak)]) were analyzed as potential variables influencing T(end). RESULTS: W(peak), VO2(peak), VO2(VT), MVC, leg fatigue at end of CET, and HR(CET)/HR(peak) were lower in group 1 than in group 2 or 3 (p≤0.05). VO2(VT) and leg fatigue at end of CET independently predicted T(end) in multiple regression analysis (r = 0.50, p = 0.001). CONCLUSION: T(end) was independently related to the aerobic fitness and to tolerance to leg fatigue at the end of exercise. A large fraction of the variability in T(end) was not explained by the physiological parameters assessed in the present study. Individualization of exercise intensity during CET should help in reducing variations in T(end) among patients with COPD.
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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 | 0.001 | 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".