Health characteristics of patients with asthma, COPD and asthma–COPD overlap in the NHANES database
Bibliographic record
Abstract
Introduction: Asthma and COPD have overlapping characteristics. As there are limited data on whether asthma–COPD overlap (ACO) represents a distinct condition, this study aimed to determine the similarities and differences of ACO with asthma and COPD. Methods: US population-based, cross-sectional study using National Health and Nutrition Examination Survey data (2009–2012) compared participants with ACO vs those with asthma or COPD, each as mutually exclusive disease states. Demographics, health status, disability/limitations, health care resource utilization, clinical characteristics, and peripheral blood eosinophil counts were analyzed. Results: A total of 1,609, 479, and 299 participants with asthma, COPD, and ACO, respectively, were included. An age-matched asthma subgroup included 299 participants from the asthma group. Compared with asthma and COPD, participants with ACO had worse health status, increased disease burden, and more comorbid conditions. The ACO, vs age-matched asthma subgroup, had lower percent predicted prebronchodilator forced expiratory volume in 1 second (82.1% vs 88.0%; P =0.017). The ACO group had significantly more asthma attacks in the past year than the age-matched asthma subgroup (49.8% vs 38.4%; P <0.001). The ACO group had more participants with postbronchodilator forced expiratory volume in 1 second <80% predicted (52.1%) vs COPD (30.8%; P =0.003) and more participants with blood eosinophil counts ≥400 cells/µL (16.9%) vs COPD (9.5%; P =0.007) and the asthma subgroup (6.7%; P =0.014). Conclusion: The ACO group represents an important subset of patients with chronic respiratory disease with an increased burden of disease over asthma and COPD individually. Early identification of this population will enable appropriate therapeutic interventions in a timely manner. Keywords: lung function, health status, eosinophils, asthma attack, respiratory disease
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".