Ethnic differences in spirometry measurements in China: Results from a large community‐based epidemiological study
Bibliographic record
Abstract
ABSTRACT Background and objective No previous studies have examined differences in spirometry measurements among ethnic populations in China, and factors which may influence ethnic differences are unclear. Our study aimed to investigate whether forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) differ among Han Chinese and other ethnic minorities in China. Methods We recruited 7137 individuals aged 35–70 years from four areas of China inhabited by ethnic minority groups between 2007 and 2009. We conducted spirometry tests for all available participants, and compared FEV1 and FVC among Uygur, Hui, Mongolian, Dai and Han Chinese ethnicities, using nonlinear multiplicative regression models. Results A total of 2005 healthy never‐smokers were enrolled in the analysis. For all ethnicities, spirometry values increased with height and decreased with age; FEV1 and FVC were consistently higher in males than in females. Compared with Han Chinese, FEV1 was 4.42% (95% CI: 2.11–6.78%) higher in Mongolians, 4.08% (95% CI: 1.33–6.76%) lower in Uygurs, 4.39% (95% CI: 1.33–7.35%) lower in Hui people and 4.72% (95% CI: 1.80–7.55%) lower in Dai people, after adjusted for potential confounders including height, age, sex and place of residence. We observed similar differences for FVC. Conclusions We detected significant differences in spirometry measurements among ethnic populations in China. Such differences cannot be fully explained by demographic, anthropometric or socioeconomic factors, but may also be attributed to genetic background as well as indoor and outdoor environmental exposures that need further investigation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".