Burden of disease associated with a COPD eosinophilic phenotype
Bibliographic record
Abstract
Purpose: Based on blood and sputum samples, up to 40% of patients with COPD have eosinophilic inflammation; however, there is little epidemiology data characterizing the health care burden within this sub-population. Given that COPD-attributable medical costs in the USA are predicted to approach $50 billion by 2020, we analyzed the effect of blood eosinophil counts and exacerbations on health care resource utilization and costs. Patients and methods: This cross-sectional study used electronic medical records and insurance claims data from the Reliant Medical Group (January 2011-December 2015). Eligible patients were ≥40 years of age, continuously enrolled during the year of interest (2012, 2013, 2014, or 2015), had ≥1 COPD-related code in the preceding year, and documented maintenance therapy use. Patients with ≥1 blood eosinophil count recorded were stratified into 2 cohorts: <150 cells/µL and ≥150 cells/µL. Endpoints included demographics, clinical characteristics, health care resource utilization, and costs. The impact of blood eosinophil count and exacerbation patterns on health care resource utilization and costs was assessed with multivariate analyses. Results: On average, 2,832 eligible patients were enrolled annually, of whom ~28% had ≥1 eosinophil count recorded during the year. The ≥150 cells/µL cohort had numerically higher all-cause and COPD-related health care resource utilization and cost each year compared with the <150 cells/µL cohort, but varied by service and year. Among patients with exacerbations, the ≥150 cells/µL cohort exhibited significantly higher COPD-related costs compared with the <150 cells/µL cohort. Conclusion: Blood eosinophil counts may be a useful biomarker for burden of disease in a subgroup of patients with COPD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".