Should We View Chronic Obstructive Pulmonary Disease Differently after ECLIPSE?. A Clinical Perspective from the Study Team
Bibliographic record
Abstract
RATIONALE: Chronic obstructive pulmonary disease (COPD) seems to be a heterogeneous disease with a variable course. OBJECTIVES: We wished to characterize the heterogeneity and variability of COPD longitudinally. METHODS: In the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study of 2,164 patients with clinically stable COPD, 337 smokers with normal lung function, and 245 never-smokers, we measured a large number of clinical parameters, lung function, exercise tolerance, biomarkers, and amount of emphysema by computed tomography. All three groups were followed for 3 years. MEASUREMENTS AND MAIN RESULTS: We found a striking heterogeneity among patients with COPD, with poor correlations between FEV1, symptoms, quality of life, functional outcomes, and biomarkers. Presence of systemic inflammation was found in only a limited proportion of patients, and did not relate to baseline characteristics or disease progression, but added prognostic value for predicting mortality. Exacerbations tracked over time and added to the concept of the "frequent exacerbator phenotype." Disease course was very variable, with close to a third of patients not progressing at all. Risk factors for 3-year change in both FEV1 and lung density were assessed. For FEV1 decline, continued smoking and presence of emphysema were the strongest predictors of progression; club cell protein was found to be a potential biomarker for disease activity. For progression of emphysema, the strongest predictors were continued smoking and female sex. CONCLUSIONS: By following a large, well characterized cohort of patients with COPD over 3 years, we have a clearer picture of a heterogeneous disease with clinically important subtypes ("phenotypes") and a variable and not inherently progressive course. Clinical trial registered with www.clinicaltrials.gov (NCT00292552).
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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 | 0.052 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".