Discrepancies Between Observed and Self-Classified Race in Spirometry Interpretation: Insights from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
<bold>Background:</bold> An underexplored issue with race-specific lung function reference equations is the inaccuracies in collecting race data. We evaluated disproportionate impact of these inaccuracies on visible minorities. <bold>Methods:</bold> Using 2011-2021 data from the Canadian Longitudinal Study on Aging, we compared patients’ self-classified race with race observed by spirometry lab technicians and evaluated impacts of racial misclassification on FEV<sub>1</sub> Z-scores calculated with GLI-2012 equations. We used survey logistic regression to study the association between self-classified race and racial misclassification, controlling for age, sex, province, and social standing. <bold>Results:</bold> Among 21,319 individuals with high-quality spirometry, self-classified and observed race varied in 1% of White participants, while it ranged from 19% to 77% among visible minority groups (Figure 1) with differential impact on FEV<sub>1</sub> Z-scores (Figure 2). <bold>Conclusions:</bold> Racial misclassifications can make race-specific reference equations disproportionately less accurate for many visible minority groups. <fig><object-id>erj;66/suppl_69/PA3720/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> <fig><object-id>erj;66/suppl_69/PA3720/F2</object-id><object-id>F2</object-id><object-id>F2</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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".