Do “static” lung volumes add to inspiratory capacity to predict exertional dyspnea and poor exercise tolerance in COPD?
Bibliographic record
Abstract
Background: Reduced limits for tidal expansion (i.e., a low (↓) inspiratory capacity (IC)) is the strongest predictor of exertional dyspnea in COPD. It is conceivable that a given IC would signal greater dyspnea when starting from a higher “floor” (i.e., ↑ functional residual capacity (FRC)) towards a higher “ceiling” (i.e., ↑ total lung capacity (TLC)). Aim: To investigate the influence of FRC and TLC on the relationship between IC, exertional dyspnea, and exercise tolerance in patients showing preserved (↔) or ↓ IC. Methods: 344 patients with mild to end-stage COPD underwent pulmonary function and incremental exercise tests. GLI z-scores judged volumes9 normalcy. Low submaximal breathing reserve indicated reduced ventilatory reserves. IC/TLC≤0.34 indicated a “high risk” for all-cause mortality (Neder et al.PMID27077955). Results: ↓ IC was associated with higher dyspnea/ventilation (V̇E) and lower peak O2 uptake (V̇O2), regardless of FRC and TLC. However, a quarter of patients with ↔ IC showed ↑ FRC and ↑ TLC: they were significantly more dyspneic and impaired than their counterparts with equally-↔ IC (p<0.01) (Figure). Conclusions: Although measurements of “static” lung volumes add little to ↓ IC, one out of four patients with ↔ IC present with high dyspnea burden, poor exercise tolerance, and increased risk of a negative outcome. ↑ FRC and ↑ TLC are valuable to identify these patients.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".