Physiological phenotypes of COPD: the role of resting lung volumes
Bibliographic record
Abstract
Background: Resting “static” lung volumes are more closely related to the physiological underpinnings of COPD than FEV1. Their role in distinguishing sub-sets of patients sharing similar phenotypical features as pertaining to exertional dyspnoea and exercise tolerance remains to be demonstrated. Methods: 299 patients (172 males) underwent body plethysmography and cardiopulmonary exercise testing. Functional residual capacity (FRC) and total lung capacity (TLC)>upper limit of normal established lung and thoracic hyperinflation (LH and TH, respectively). Results: The following patterns were identified: (-)LH-(-)TH (“A”; N= 84) (-)LH-(+)TH (“B”; N= 23), , (+)LH-(-)TH (“C”; N= 39), (+)LH-(+)TH (“D”; N= 153). Worse exertional dyspnoea and poorer exercise tolerance were found in “C” and “D” (lower inspiratory capacity (IC)) whereas “B” was less symptomatic and impaired than “A” (Figure; p<0.05). Of note, “D” patients with very severe LH (FRC>170%) and TH (TLC>130%) showed higher IC than their counterparts with less LH and TH. This apparent advantage for tidal volume expansion, however, was not translated into lower dyspnoea and better exercise tolerance compared to the other groups (p>0.05). Conclusion: Resting measurements of “static” lung volumes are clinically useful to discriminate specific physiological phenotypes which are highly predictive of key patient-related outcomes: activity-related dyspnoea and exercise intolerance.
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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 | 0.002 |
| 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 | 0.001 | 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".