The effect of health disparities on racial gaps in lung function
Bibliographic record
Abstract
<bold>Background:</bold> Racialized populations disproportionately experience exposures that impair lung function. Race-neutral reference equations mitigate bias due to disparities at the cost of reduced precision. However, it is unclear what proportion of racial gaps in lung function is due to known disparities. <bold>Hypothesis:</bold> Measurable disparities will help explain racial gaps in lung function. <bold>Methods:</bold> We defined reference populations from 20,050 Black and White NHANES 2007-2012 participants. Starting with non-smokers without respiratory symptoms or diagnoses, we sequentially excluded those with occupational exposure to dust/fumes, maternal tobacco use, second-hand smoke exposure, obesity, low physical activity, unhealthy diet, self-assessed poor health, and no insurance. Across populations, we compared average age-, sex-, and height-adjusted differences in FEV<sub>1</sub> and FVC between Black and White adults (≥20) and youth (<20). <bold>Results:</bold> From the base reference population to the most restrictive, the percentage of Black participants decreased from 20% to 16% in youth and 14% to 7% in adults. The racial gap in lung function was reduced from 0.40 L (95%CI 0.36-0.43) to 0.31 L (95%CI 0.26-0.36) in FEV<sub>1</sub> and from 0.48 L (95%CI 0.44-0.51) to 0.36 (95%CI 0.30-0.42) in FVC in youth but increased in adults. <bold>Conclusions:</bold> The disparities investigated herein explain up to 25% of the racial gap in lung function among American youth. Further research on adults is required. <fig><object-id>erj;64/suppl_68/OA5572/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig>
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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 | 0.013 | 0.005 |
| 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 | 0.000 |
| 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".