Spirometry at different Latin American altitudes: a Global Lung Function Initiative project
Bibliographic record
Abstract
Aim Approximately 20% of people in Latin America live more than 2500 m above sea level. In this ethnically and socioeconomically diverse population, it is challenging to differentiate the effects of altitude from population differences in lung function. The aim of the present study was to quantify the contribution of altitude on the variability in lung function measured by spirometry in Latin America. Methods Data from healthy individuals living in nine cities across Latin America (from sea level to >2500 m above sea level) were collated. Z -scores for forced expiratory volume in 1 s (FEV 1 ), forced vital capacity (FVC) and FEV 1 /FVC were calculated using available reference equations. Mixed-effects linear regression models were used to quantify the variance in spirometry explained by altitude. The percentage of individuals that were below the lower limit of normal (fifth percentile) were summarised. Results A total 4480 subjects (3–94 years) were included. Average FEV 1 and FVC z -scores differed between the altitude groups, whereas FEV 1 /FVC did not. After adjusting for sex, height and age, altitude explained up to ∼18% of the variability in lung function measured by spirometry. Conclusion For people living at altitude, existing approaches to interpreting spirometry measures may misclassify individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".