A Research Agenda to Improve Outcomes in Patients with Chronic Obstructive Pulmonary Disease and Cardiovascular Disease: An Official American Thoracic Society Research Statement
Bibliographic record
Abstract
Abstract Rationale Individuals with chronic obstructive pulmonary disease (COPD) are often at risk for or have comorbid cardiovascular disease and are likely to die of cardiovascular-related causes. Objectives To prioritize a list of research topics related to the diagnosis and management of patients with COPD and comorbid cardiovascular diseases (heart failure, atherosclerotic vascular disease, and atrial fibrillation) by summarizing existing evidence and using consensus-based methods. Methods A literature search was performed. References were reviewed by committee co-chairs. An international, multidisciplinary committee, including a patient advocate, met virtually to review evidence and identify research topics. A modified Delphi approach was used to prioritize topics in real time on the basis of their potential for advancing the field. Measurements and Main Results Gaps spanned the translational science spectrum from basic science to implementation: 1) disease mechanisms; 2) epidemiology; 3) subphenotyping; 4) diagnosis and management; 5) clinical trials; 6) care delivery; 7) medication access, adherence, and side effects; 8) risk factor mitigation; 9) cardiac and pulmonary rehabilitation; and 10) health equity. Seventeen experts participated, and quorum was achieved for all votes (>80%). Of 17 topics, ≥70% agreement was achieved for 12 topics after two rounds of voting. The range of summative Likert scores was −15 to 25. The highest priority was “Conduct pragmatic clinical trials with patient-centered outcomes that collect both pulmonary and cardiac data elements.” Health equity was identified as an important topic that should be embedded within all research. Conclusions We propose a prioritized research agenda with the purpose of stimulating high-impact research that will hopefully improve outcomes among people with COPD and cardiovascular disease.
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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 | 0.282 | 0.162 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.026 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".