Development of the Advancing the Patient Experience in COPD Registry: A Modified Delphi Study
Bibliographic record
Abstract
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is commonly managed by family physicians, but little is known about specifics of management and how this may be improved. The Advancing the Patient Experience in COPD (APEX COPD) registry will be the first U.S. primary care, health system-based registry following patients diagnosed with COPD longitudinally, using a standardized set of variables to investigate how patients are managed in real life and assess outcomes of various management strategies. OBJECTIVE: Gaining expert consensus on a standardized list of variables to capture in the APEX COPD registry. METHODS: A modified, Delphi process was used to reach consensus on which data to collect in the registry from electronic health records (EHRs), patient-reported information (PRI) and patient-reported outcomes (PRO), and by physicians during subsequent office visits. The Delphi panel comprised 14 primary care and specialty COPD experts from the United States and internationally. The process consisted of 3 iterative rounds. Responses were collected electronically. RESULTS: Of the initial 195 variables considered, consensus was reached to include up to 115 EHR variables, 34 PRI/PRO variables and 5 office-visit variables in the APEX COPD registry. These should include information on symptom burden, diagnosis, COPD exacerbations, lung function, quality of life, comorbidities, smoking status/history, treatment specifics (including side effects), inhaler management, and patient education/self-management. CONCLUSION: COPD experts agreed upon the core variables to collect from EHR data and from patients to populate the APEX COPD registry. Data will eventually be integrated, standardized and stored in the APEX COPD database and used for approved COPD-related research.
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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 | 0.194 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".