Using Pulmonary Imaging to Move Chronic Obstructive Pulmonary Disease beyond FEV1
Bibliographic record
Abstract
FEV1, measured using spirometry, provides a straightforward, widely available, and inexpensive global measurement of airflow limitation and lung function. For decades, FEV1 has remained the main intermediate endpoint used in research studies and for the development of new chronic obstructive pulmonary disease (COPD) therapies. Not surprisingly, treatments that acutely improve FEV1 dominate as COPD therapies. However, in patients with COPD, the relationship of FEV1 with symptoms and outcomes such as exacerbations and mortality is weak, and, importantly, FEV1 does not take into account the heterogeneity of COPD or its different phenotypes. Thoracic imaging provides a way to quantify airway remodeling, emphysematous destruction, regional ventilation abnormalities (ventilation defects), and gas trapping in ex-smokers in whom FEV1 may be normal and in patients with COPD with very modest lung function deterioration. In individual patients and in COPD cohort studies, thoracic imaging using X-ray computed tomography, and magnetic resonance imaging (conventional (1)H as well as hyperpolarized noble gases such as (129)Xe, (3)He, and inhaled O2 and (19)F) can be used to directly visualize the structural and functional consequences of COPD and thus provide a clearer picture of COPD mechanisms, disease progression, and response to therapy. We briefly describe pulmonary imaging methods that provide a way to visualize and quantify, with high spatial and temporal resolution, regional ventilation abnormalities, gas trapping, emphysema, and airway remodeling in COPD. Finally, we discuss the implications of recent imaging findings and their impact on future biomarker and therapy research aimed at improving COPD 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".