Wasted ventilation in mild COPD: protocol for a clinical physiology and functional imaging study
Bibliographic record
Abstract
Background High ventilation–CO2output, signaling increased wasted ventilation in the physiological dead space (VDphys), has a dominant role in eliciting activity-related dyspnoea in subjects with COPD showing only mild airflow obstruction. Exposing the mechanisms driving wasted ventilation is key to advancing the field towards new therapeutic approaches to improve dyspnoea and exercise tolerance in this growing patient sub-population. Central hypothesis Increased areas of high alveolar ventilation (V̇A)/capillary perfusion (Q̇c) due to impairedQ̇cin nonemphysematous, non-air trapping areas of the lungs add to any underlying emphysema to increase wasted ventilation and exertional dyspnoea in mild COPD. Methods 40 patients (20 women) showing post-bronchodilator forced expiratory volume in 1 s ≥70% predicted and 20 sex- and age-matched controls will perform constant load exercise tests in a flutter-kicking device in the physiology laboratory and, in another visit, a magnetic resonance imaging (MRI) facility. Volumetric capnography will be used to breath-by-breath quantify rest and exerciseVDphys. Free-breathing, noncontrast proton MRI (phase-resolved functional lung (PREFUL))V̇A,Q̇candV̇A/Q̇c,maps will be co-registered to emphysema and air trapping severity and distribution as established by computed tomography (parametric response mapping). Study implications Confirmation of the study's main hypothesis will provide the first evidence that increased wasted ventilation in dyspnoeic patients with mild COPD is not a mere consequence of emphysematous destruction being also reflective of impaired perfusion across apparently preserved lung tissue. This will set the stage for subsequent interventional studies geared to address this potentially treatable disease trait.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.024 |
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".