Clinical relevance of abnormal “dynamic” and “static” lung volumes in patients with COPD
Bibliographic record
Abstract
Background: The clinical utility of “slow” spirometry and plethysmography in COPD remains unclear, and the GOLD guidelines (Eur Respir J 2023;61:2300239) do not formally recommend their routine use. Aims: To assess the additive value of inspiratory capacity (IC) and static lung volumes (TLC, RV) in predicting clinical outcomes in COPD. Methods: Symptomatic adults with COPD (FEV₁/FVC <0.70) were recruited from a tertiary outpatient clinic. Abnormal ( ULN as appropriate) pulmonary function parameters (FEV1, IC, TLC, RV) were entered into (backward) logistic regression models to predict clinical outcomes. mMRC and 6MWD were classified according to BODE categories ≥2 to define severity (NEJM 2004;350:1005). CAT score ≥10 define a high respiratory burden (GOLD). Results: 197 individuals were included (52% ♀; 65±8 years; FEV₁ 15–97% predicted). Reduced IC ( ULN) and air trapping (RV>ULN) were found in 144 (73%), 63 (32%), and 151 (77%) subjects, respectively. A ↓ IC capacity [OR=2.34 (1.11-4.91)] and ↑ TLC [OR=2.23 (1.09-4.57)] were the variables remaining in the model to predict severe dyspnoea (mMRC≥3) or severe exercise capacity impairment (6MWD<250m) (Panel A). ↓ IC capacity [OR=3.62 (1.57-8.34)] was the unique predictor of high respiratory burden (Panel B). erj;66/suppl_69/PA4182/F1 F1 F1 Conclusion: Resting lung volume measurements contribute to discriminate COPD patients with worse clinical outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".