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Implementation of Race-Neutral Spirometry Reference Equations Change the Interpretation of Tests in South East Asian and Black Pediatric Populations

2025· article· en· W4410276287 on OpenAlexaff
Josie Chrenek, Ash Sandhu, Jaspreet Dhillon, David Wensley, S.D. Dell

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineSpirometryInterpretation (philosophy)Race (biology)Reference valuesInternal medicineGender studiesAsthmaLinguistics

Abstract

fetched live from OpenAlex

Abstract RATIONALE: The 2012 Global Lung Function Initiative (GLI) spirometry reference equations rely on race to predict pulmonary function. Updated 2023 American Thoracic Society/European Respiratory Society guidelines now recommend race-neutral GLI Global equations to mitigate issues with the lack of a standardized race definition, the clinical implications of disproportionate spirometry standards between racial groups, and the overrepresentation of Caucasian populations in the equation derivation. While Black adults show improved interpretations with the race-specific equations compared to race-neutral, the inverse is found in Caucasian populations. However, these impacts remain understudied in children and non-Black populations of color. Here, we evaluate the differences in percentage and severity of pediatric lung function impairments identified using the race-specific compared to race-neutral GLI equations. METHODS: Retrospective spirometry data and self-identified race for BC Children's Hospital patients aged 5-18 years from August 2022 to June 2024 were interpreted using the race-specific and race-neutral GLI equations. Impairment severity was assessed with forced expiratory volume in one second (FEV1) z-scores. FEV1, forced vital capacity (FVC), and FEV1/FVC lower limits of normal were used to classify tests as normal, obstructive, suspected restrictive, or suspected mixed. RESULTS: Spirometry results from 1759 Caucasian, 44 Black, 31 North East Asian, 421 South East Asian, and 691 Other/Mixed individuals were analyzed. Compared to the race-specific, the race-neutral equations identified increased impairment severity in 22.7% (95% CI, 11.5%-37.8%) of Black and 3.3% (95% CI, 1.8%-5.5%) of South East Asian individuals. Decreased severity was found in 13.5% (95% CI, 11.9%-15.2%) of Caucasian and 16.1% (95% CI, 5.5%-33.7%) of North East Asian individuals, with fewer changes in the Other/Mixed group. Percentage of suspected restriction increased in Black individuals from 20.5% (95% CI, 9.8%-35.3%) to 43.2% (95% CI, 28.3%-59.0%) and decreased in Caucasian individuals from 14.4% (95% CI, 12.8%-16.2%) to 9.0% (95% CI, 7.7%-10.5%) using the race-neutral equations. Among the South East Asian group, percentage of obstructive impairments decreased from 17.1% (95% CI, 13.6%-21.0%) to 7.8% (95% CI, 5.5%-10.8%), corresponding to a decreased proportion meeting asthma diagnostic criteria from 6.9% (95% CI, 4.2%-10.6%) to 3.6% (95% CI, 1.8%-6.6%). CONCLUSION: Compared to the race-specific, the race-neutral GLI equations produced the largest increase in severity and suspected restriction in the Black population, while the greatest decrease in obstruction was found in the South East Asian population. The diagnostic and management implications of these changes to spirometry interpretation will require consideration as the race-neutral equations are implemented clinically for pediatric patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.364
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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