FEV<sub>1</sub>Q: a race-neutral approach to assessing lung function
Bibliographic record
Abstract
Background Forced expiratory volume in 1 s quotient (FEV 1 Q) is a simple approach to spirometry interpretation that compares measured lung function to a lower boundary. This study evaluated how well FEV 1 Q predicts survival compared with current interpretation methods and whether race impacts FEV 1 Q. Methods White and Black adults with complete spirometry and mortality data from the National Health and Nutrition Examination Survey (NHANES) III and the United Network for Organ Sharing (UNOS) database for lung transplant referrals were included. FEV 1 Q was calculated as FEV 1 divided by 0.4 L for females or 0.5 L for males. Cumulative distributions of FEV 1 were compared across races. Cox proportional hazards models tested mortality risk from FEV 1 Q adjusting for age, sex, height, smoking, income and among UNOS individuals, referral diagnosis. Harrell's C-statistics were compared between absolute FEV 1 , FEV 1 Q, FEV 1 /height 2 , FEV 1 z-scores and FEV 1 % predicted. Analyses were stratified by race. Results Among 7182 individuals from NHANES III and 7149 from UNOS, 1907 (27%) and 991 (14%), respectively, were Black. The lower boundary FEV 1 values did not differ between Black and White individuals in either population (FEV 1 first percentile difference ≤0.01 L; p>0.05). Decreasing FEV 1 Q was associated with increasing hazard ratio (HR) for mortality (NHANES III HR 1.33 (95% CI 1.28–1.39) and UNOS HR 1.18 (95% CI 1.12–1.23)). The associations were not confounded nor modified by race. Discriminative power was highest for FEV 1 Q compared with alternative FEV 1 approaches in both Black and White individuals. Conclusions FEV 1 Q is an intuitive and simple race-neutral approach to interpreting FEV 1 that predicts survival better than current alternative methods.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".