Assessing spirometric parameters in children with sickle cell disease: GLI Global vs. race-specific normative equations
Bibliographic record
Abstract
Introduction: The recent ATS recommendations on the use of GLI Global equations for spirometry interpretation may have a significant impact on interpretation of spirometry results and subsequent patient management, particularly in at-risk populations, such as patients with sickle cell disease. Objectives: We aimed to determine the impact of GLI Global vs. race specific equations on spirometric parameters and classification in paediatric patients with sickle cell disease. Methods: This was a retrospective review of spirometry tests performed for sickle cell disease at the Hospital for Sick Children in Toronto, Canada from 2021 to 2024. The first spirometry test for each patient, which met ATS acceptability and reproducibility criteria, was included. Spirometry was interpreted using both GLI Race-specific and GLI Global normative equations. Results: 215 spirometry tests were included, with a mean patient age of 13.5 years (SD 2.5), 52% female. GLI Global resulted in significantly lower FEV1 and FVC z-scores compared with Race-specific equations (mean FEV1 Race-specific vs. Global -0.75 vs. -1.42, p<0.0001; FVC -0.53 vs. -1.22, p<0.0001, paired t-test). GLI Global resulted in more spirometry tests suggestive of restriction (14.4% vs. 31.6%), and fewer classified as normal (67% vs. 47.4%). Discussion: GLI Global normative equations resulted in fewer spirometry tests classified as normal, and more tests suggestive of restriction. Next steps in our analysis will assess the clinical implications of this 20% decrease in normal tests in this population, as well as the association of normative equation choice and interpretation of bronchodilator response testing.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".