Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE)
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and not well understood. The forced expiratory volume in one second is used for the diagnosis and staging of COPD, but there is wide acceptance that it is a crude measure and insensitive to change over shorter periods of time. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) is a 3-yr longitudinal study with four specific aims: 1) definition of clinically relevant COPD subtypes; 2) identification of parameters that predict disease progression in these subtypes; 3) examination of biomarkers that correlate with COPD subtypes and may predict disease progression; and 4) identification of novel genetic factors and/or biomarkers that both correlate with clinically relevant COPD subtypes and predict disease progression. ECLIPSE plans to recruit 2,180 COPD subjects in Global Initiative for Chronic Obstructive Lung Disease categories II-IV and 343 smoking and 223 nonsmoking control subjects. Study procedures are to be performed at baseline, 3 months, 6 months and every 6 months thereafter. Assessments include pulmonary function measurements (spirometry, impulse oscillometry and plethysmography), chest computed tomography, biomarker measurement (in blood, sputum, urine and exhaled breath condensate), health outcomes, body impedance, resting oxygen saturation and 6-min walking distance. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points is the largest study attempting to better describe the subtypes of chronic obstructive pulmonary disease, as well as defining predictive markers of its progression.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".