Effect of altitude on spirometry values in Latin American: A GLI Network ERS Clinical Research Collaboration
Bibliographic record
Abstract
The Latin American population is widely diverse in race and ethnicity; many individuals identified as mixed ethnicity makes selection of spirometry reference equations difficult. Additionally, 17% of Latin Americans live above 2500 meters above sea level which may influence lung development during childhood. Our aim was to investigate the role of altitude on spirometry, and whether a race-neutral reference equation (GLI-Global) fits subjects living at different altitudes. Methods: Data from 9 cities, classified as low (≤1500 m), moderate (1500-2500 m), and high (≧ 2500 m) altitude were collected. Z-scores for FEV1, FVC and FEV1/FVC were expressed using GLI-Global equations. Mixed-effects regression models were used to describe the differences in lung function across altitude adjusted for height, weight, BMI, sex, and age. Results: 4480 ‘healthy’ individuals (3 to 94 years) were included. Average FEV/FVC z-score did not differ between the three altitude groups, whereas at higher altitudes, GLI-Global underestimates FEV1 and FVC (Fig 1). Adjusted for other factors, altitude explained up to 32% of the variability in spirometry. Conclusion: Individuals at high altitude are likely to have their FEV1 and FVC underestimated using GLI-Global reference equations. Further work is needed to elucidate why people living at altitude have larger than predicted lung function.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".