Ventilation Distribution Heterogeneity at Rest as a Marker of Exercise Impairment in Mild-to-Advanced COPD
Bibliographic record
Abstract
The difference between total lung capacity (TLC) by body plethysmography and alveolar volume (VA) from the single-breath lung diffusing capacity measurement provides an index of ventilation distribution inequalities in COPD. The relevance of these abnormalities to dyspnea and exercise intolerance across the continuum of disease severity remains unknown. Two-hundred and seventy-six COPD patients distributed across GOLD grades 1 to 4 and 67 healthy controls were evaluated. The "poorly communicating fraction" (PCF) of the TLC was estimated as the ratio (%) of TLC to VA. Healthy subjects showed significantly lower PCF values compared to GOLD grades 1 to 4 (10 ± 3% vs. 17 ± 8% vs. 27 ± 10% vs. 37 ± 10% vs. 56 ± 11%, respectively; p < 0.05). Pulmonary gas exchange impairment, mechanical ventilatory constraints and ventilation-corrected dyspnea scores worsened across PCF tertiles (p < 0.05). Of note, GOLD grades 1 and 2 patients with the highest PCF values had pronounced exercise ventilatory inefficiency and dyspnea as a limiting symptom. In fact, dyspnea was a significant contributor to exercise limitation only in those with "moderate" or "extensive" PCF (p < 0.05). A receiver operating characteristics curve analysis revealed that PCF was a better predictor of severely reduced maximal exercise capacity than traditional pulmonary function indexes including FEV1 (area under the curve (95% confidence interval) = 0.85 (0.81-0.89), best cutoff = 33.4%; p < 0.01). In conclusion, PCF is a readily available functional marker of gas exchange and mechanical abnormalities relevant to dyspnea and exercise intolerance across the COPD grades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".