Prognostic value of a low resting inspiratory capacity and reduced inspiratory muscle strength in COPD
Bibliographic record
Abstract
Background: Low resting inspiratory capacity (IC, index of hyperinflation) and low maximal inspiratory pressure (MIP, index of inspiratory muscle weakness) have been previously linked to exertional dyspnea, exercise limitation and poor survival in COPD. The relative contributions of these two inextricably linked variables to important clinical outcomes are unknown. Aim: To examine the interaction between resting IC and MIP (both % predicted) with exertional dyspnea, exercise capacity and long-term survival in patients with COPD. Methods: 288 patients with mild to advanced COPD completed standard lung function testing and a cycle cardiopulmonary exercise test. Multiple linear regression determined predictors of the exertional dyspnea-ventilation slope and peak oxygen uptake (V̇O2peak). Cox regression determined predictors of 10-year all-cause mortality. Results: A significant association between IC and the dyspnea-ventilation slope was observed (standardized β=-0.44, p<0.001), while MIP was excluded from the regression model (p=0.713). IC and MIP were included in the final model to predict V̇O2peak. However, the standardized β was greater for IC (0.49) than MIP (0.22). After adjusting for age and sex, IC was independently associated with 10-year all-cause mortality (hazard ratio=1.016, confidence interval5-95%=1.010-1.017, p=0.004), while forced expiratory volume in 1 second, MIP and diffusing capacity for carbon monoxide (all % predicted) were excluded (all p>0.05). Conclusions: Low resting IC, but not MIP, was consistently linked to dyspnea burden, low V̇O2peak and worse survival in COPD. These results support the use of resting IC as an important physiological biomarker in COPD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".