Understanding racial differences of COPD patients with an ecological model: two large cohort studies in the US and Korea
Bibliographic record
Abstract
BACKGROUND: There are limited data about the racial difference in the characteristics of chronic obstructive pulmonary disease (COPD) patients who are treated at clinics. We aimed to compare sociodemographic and clinical characteristics between US and Korean COPD patients using large-scale nationwide COPD cohorts. METHODS: = 4477, 3461 were non-Hispanic whites [NHW], and 1016 were African Americans [AA]). RESULTS: Compared to NHW, AA had a significantly lower adjusted prevalence ratio (aPR) of cough >3 months (aPR: 0.67; 95% CI [confidence interval]: 0.60-0.75) and phlegm >3 months (aPR: 0.78, 95% CI: 0.70-0.86), but higher aPR of dyspnea (modified Medical Round Council scale ⩾2) (aPR: 1.22; 95% CI: 1.15-1.29), short six-minute walk distance (<350 m) (aPR: 1.98; 95% CI: 1.81-2.14), and poor quality of life (aPR: 1.10; 95% CI: 1.05-1.15). Compared to NHW, Koreans had a significantly lower aPR of cough >3 months (aPR: 0.53; 95% CI: 0.47-0.59), phlegm >3 months (aPR: 0.75; 95% CI: 0.67-0.82), dyspnea (aPR: 0.72; 95% CI: 0.66-0.79), and moderate-to-severe acute exacerbation in the previous year (aPR: 0.73; 95% CI: 0.65-0.82). NHW had the highest burden related to chronic bronchitis symptoms and cardiovascular diseases related to comorbidities. CONCLUSION: There are substantial differences in sociodemographic characteristics, clinical presentation, and comorbidities between COPD patients from the KOCOSS and COPDGene, which might be caused by interactions between various intrapersonal, interpersonal, and environmental factors of the ecological model. Thus, a broader and more comprehensive approach would be necessary to understand the racial differences of COPD patients.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".