Applicability of the Global Lung Function Initiative prediction equations in Hong Kong Chinese children
Bibliographic record
Abstract
Abstract Background and Objective This study aimed to assess the applicability of the Global Lung Function Initiative (GLI) prediction equations for spirometry in Hong Kong children and to develop prediction equations based on the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) modeling. Methods Healthy Chinese children and adolescents aged 6–17 years old were recruited from randomly selected schools to undergo spirometry. The measurements were transformed to z‐score according to the GLI‐2012 equations for South East (SE) Asians and the GLI‐2022 global race‐neutral equations. Prediction equations for spirometric indices were developed with GAMLSS modeling to identify predictors. Results A total of 886 children (477 boys) with a mean age of 12.5 years (standard deviation [SD] 3.3 years) were included. By the GLI‐2012 SE Asian equations, positive mean z‐scores were observed in forced expiratory volume in 1 s (FEV1) (boys: 0.138 ± SD 0.828; girls: 0.206 ± 0.823) and forced vital capacity (FVC) (boys: 0.160 ± 0.930; girls: 0.310 ± 0.895) in both sexes. Negative mean z‐scores were observed in FEV1/FVC ratio (boys: −0.018 ± 0.998; girls: −0.223 ± 0.897). In contrast, negative mean z‐scores in FEV1 and FVC, and positive mean z‐scores in FEV1/FVC were observed when adopting the GLI‐2022 race‐neutral equations. The mean z‐scores were all within the range of ±0.5. By GAMLSS models, age and height were significant predictors for all four spirometric indices, while weight was an additional predictor for FVC and FEV1. Conclusion Our study provided data supporting the applicability of the GLI prediction equations in Hong Kong Chinese children. The GLI‐2012 equations may underestimate FEV1 and FVC, while the GLI‐2022 equations may overestimate the parameters, but the differences lie within the physiological limits. By GAMLSS modeling, weight was an additional predictor for FVC and FEV1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".