Effects of implementing GLI-2012 reference equations on pulmonary function test (PFT) interpretations
Bibliographic record
Abstract
Background: Race/ethnicity are important features in determination of normal reference values according to the Global Lung Function Initiative (GLI-2012) guidelines. The PFT laboratories in our center use Canadian reference equations (CRE) derived from a healthy Caucasian cohort (Gutierrez C, et al. Can Respir J 2004;11:414). The appropriateness of applying CRE to the multiethnic population in Canada has not been assessed. Objectives: To evaluate the effects of changing from CRE to GLI-2012 equations on PFT interpretation in a multiethnic population, and to identify ethnic groups where discrepant interpretations are common. Methods: We applied the CRE, GLI-ethnic-based (GLI-Race), and GLI-ethnic-neutral (GLI-Other) reference equations to PFT data from 406 patients (aged 20-80 years) between 2017-2021. We compared concordance of abnormal diagnoses (FVC, FEV1, and FEV1/FVC < LLN) amongst the 3 reference sets and evaluated whether race/ethnicity was associated with discordance. Results: Of 406 participants, 43.6% were non-Caucasian. CRE led to higher rates of abnormal (< LLN) FVC and FEV1 compared to GLI-Race and GLI-Other in all groups. The discordance was highest when comparing the CRE to GLI-Race interpretations in Black, South East Asian, and Mixed/other ethnic groups. In contrast, the frequency of discordance did not differ among ethnic groups when CRE was compared with GLI-Other. There was high concordance in FEV1/FVC interpretation amongst the 3 reference equations. Conclusion: Interpretation using CRE was associated with overdiagnosis of restrictive defects in some race/ethnicity groups compared to GLI-Race.
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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 | 0.051 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".