Advancing the Patient EXperience (APEX) in COPD Registry: Study Design and Strengths
Bibliographic record
Abstract
The Advancing the Patient Experience (APEX) in Chronic Obstructive Pulmonary Disease (COPD) registry (https://www.apexcopd.org/) is the first primary care health system-based COPD registry in the United States. While its ultimate goal is to improve the care of patients diagnosed with COPD, the registry is also designed to describe real-life experiences of people with COPD, track key outcomes longitudinally, and assess the effectiveness of interventions. It will retrospectively and prospectively collect information from 3000 patients enrolled in 5 health care organizations. Information will be obtained from electronic health records, and from extended annual and brief questionnaires completed by patients before clinic visits. Core variables to be collected into the APEX COPD registry were agreed on by Delphi consensus and fall into 3 domains: demographics, COPD monitoring, and treatment. Main strengths of the registry include: 1) its size and scope (in terms of patient numbers, geographic spread and use of multiple information sources including patient-reported information); 2) collection of variables which are clinically relevant and practical to collect within primary care; 3) use of electronic data capture systems to ensure high-quality data and minimization of data-entry requirements; 4) inclusion of clinical, database development, management and communication experts; 5) regular sharing of key findings, both at international/national congresses and in peer-reviewed publications; and 6) a robust organizational structure to ensure continuance of the registry, and that research outputs are ethical, relevant and continue to bring value to both patients and physicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.117 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".