Comparison of spirometry equations (GLI 2022 vs PLATINO) in Mexican-Hispanics with asthma and COPD
Bibliographic record
Abstract
Introduction: Reference equations for spirometry are essential in evaluating chronic airway diseases, particularly among diverse populations such as Mexican-Hispanics. While the Global Lung Initiative (GLI) has introduced race-neutral equations, which improve diagnostic accuracy, its applicability in Mexican-Hispanics remains underexplored. Objectives: To evaluate and compare the performance of GLI (2022) and PLATINO reference equations among Mexican-Hispanic patients diagnosed with COPD and asthma. Methods: This observational, cross-sectional study included demographic data and spirometric measurements from patients with baseline grade A or B spirometry. Comparative analysis used a two-tailed, paired samples t-test to assess differences between GLI and PLATINO equations (p<0.05 considered significant). Results: We included 88 patients with COPD (n=38) or asthma (n=50) who underwent pre (n=88) and post (n=65) bronchodilator (BD) spirometry. Forced vital capacity (FVC) % predicted was higher with GLI than PLATINO pre-BD (86.7±23.0 vs. 77.5±20.4, p<0.001) and post-BD (98.1±20.1 vs. 87.1±17.3, p<0.001). Similarly, forced expiratory volume in 1-second (FEV1) % predicted was higher with GLI pre- (69.6±22.5 vs. 65.5±21.1, p<0.001) and post-BD (81.8±23.8 vs. 76.5±22.0, p<0.001). The lower limit of normal (LLN) FEV1/FVC ratio was also higher in GLI vs. PLATINO (0.69±0.04 vs. 0.67±0.04, p<0.001). Conclusions: Comparative analysis showed that GLI produced higher % predicted values for FEV1, FVC, and the LLN of the FEV1/FVC ratio than PLATINO. These findings suggest that GLI may underestimate respiratory disease severity in Mexican-Hispanics, potentially impacting clinical decision-making.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".