Discrepancies Between Observed and Self-Classified Race in Spirometry Interpretation: Insights from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Background: 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. Methods: 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 FEV1 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. Results: 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 FEV1 Z-scores (Figure 2). Conclusions: Racial misclassifications can make race-specific reference equations disproportionately less accurate for many visible minority groups. erj;66/suppl_69/PA3720/F1 F1 F1 erj;66/suppl_69/PA3720/F2 F2 F2
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".