A Race-neutral Approach to the Interpretation of Lung Function Measurements
Bibliographic record
Abstract
Abstract Rationale The use of self-reported race and ethnicity to interpret lung function measurements has historically assumed that the observed differences in lung function between racial and ethnic groups were because of thoracic cavity size differences relative to standing height. Very few studies have considered the influence of environmental and social determinants on pulmonary function. Consequently, the use of race and ethnicity-specific reference equations may further marginalize disadvantaged populations. Objectives To develop a race-neutral reference equation for spirometry interpretation. Methods National Health and Nutrition Examination Survey (NHANES) III data (n = 6,984) were reanalyzed with sitting height and the Cormic index to investigate whether body proportions were better predictors of lung function than race and ethnicity. Furthermore, the original GLI (Global Lung Function Initiative) data (n = 74,185) were reanalyzed with inverse-probability weights to create race-neutral GLI global (2022) equations. Measurements and Main Results The inclusion of sitting height slightly improved the statistical precision of reference equations compared with using standing height alone but did not explain observed differences in spirometry between the NHANES III race and ethnic groups. GLI global (2022) equations, which do not require the selection of race and ethnicity, had a similar fit to the GLI 2012 “other” equations and wider limits of normal. Conclusions The use of a single global spirometry equation reflects the wide range of lung function observed within and between populations. Given the inherent limitations of any reference equation, the use of GLI global equations to interpret spirometry requires careful consideration of an individual’s symptoms and medical history when used to make clinical, employment, and insurance decisions.
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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 | 0.001 |
| 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 | 0.001 |
| 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".