Measurement and Interpretation of Exercise Ventilatory Efficiency
Bibliographic record
Abstract
Cardiopulmonary exercise testing (CPET) is a method for evaluating pulmonary and cardiocirculatory abnormalities, dyspnea and exercise tolerance in healthy individuals and patients with chronic conditions. During exercise, ventilation (V̇E) increases in proportion to metabolic demand (i.e. carbon dioxide production (V̇CO2)) to maintain arterial blood gas and acid-base balance. The response of V̇E relative to V̇CO2 (V̇E/V̇CO2) is commonly termed ventilatory efficiency and is becoming a common physiological tool, in conjunction with other key variables such as operating lung volumes, to evaluate exercise responses in patients with chronic conditions. A growing body of research has shown that the V̇E/V̇CO2 response to exercise is elevated in conditions such as chronic heart failure (CHF), pulmonary hypertension (PH), interstitial lung disease and chronic obstructive pulmonary disease (COPD). Importantly, this potentiated V̇E/V̇CO2 response contributes to dyspnea and exercise intolerance. The clinical significance of ventilatory inefficiency is demonstrated by findings showing that the elevated V̇E/V̇CO2 response to exercise is an independent predictor of mortality in patients with CHF, PH and COPD. In this article, the underlying physiology, measurement, and interpretation of exercise ventilatory efficiency during CPET is reviewed. Additionally, exercise ventilatory efficiency in varying disease states is briefly discussed.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".