Patterns of care in the management of high-risk COPD in the US (2011–2019): an observational study for the CONQUEST quality improvement program
Bibliographic record
Abstract
Background: In this study, we compare management of patients with high-risk chronic obstructive pulmonary disease (COPD) in the United States to national and international guidelines and quality standards, including the COllaboratioN on QUality improvement initiative for achieving Excellence in STandards of COPD care (CONQUEST). Methods: Patients were identified from the DARTNet Practice Performance Registry and categorized into three high-risk cohorts in each year from 2011 to 2019: newly diagnosed (≤12 months after diagnosis), already diagnosed, and patients with potential undiagnosed COPD. Patients were considered high-risk if they had a history of exacerbations or likely exacerbations (respiratory consult with prescribed medication). Descriptive statistics for 2019 are reported, along with annual trends. Findings: In 2019, 10% (n = 16,610/167,197) of patients met high-risk criteria. Evidence of spirometry for diagnosis was low; in 2019, 81% (n = 1228/1523) of patients newly diagnosed at high-risk had no record of spirometry/peak expiratory flow in the 12 months pre- or post-diagnosis and 43% (n = 651/1523) had no record of COPD symptom review. Among those newly and already diagnosed at high-risk, 52% (n = 4830/9350) had no evidence of COPD medication. Interpretation: Findings suggest inconsistent adherence to evidence-based guidelines, and opportunities to improve identification, documentation of services, assessment, therapeutic intervention, and follow-up of patients with COPD. Funding: This study was conducted by the Observational and Pragmatic Research Institute (OPRI) Pte Ltd and was partially funded by Optimum Patient Care Global and AstraZeneca Ltd. No funding was received by the Observational & Pragmatic Research Institute Pte Ltd (OPRI) for its contribution.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".