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Assessing the Stability of the Healthy Never-smoking Subgroup Over 12 Years and Its Effect on Reference Equations for Lung Function in a Prospective Cohort Study: The Multi-ethnic Study of Atherosclerosis Lung Study

2025· article· en· W4410277349 on OpenAlexaff
Kristina L. Buschur, Pallavi Balte, E.A. Hermann, J.L. Hankinson, Norrina B. Allen, Alan Bertoni, James S. Pankow, Wendy S. Post, Benjamin M. Smith, K. Watson, R. Graham Barr

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineLung functionProspective cohort studyEthnic groupCohort studyCohortInternal medicineLung

Abstract

fetched live from OpenAlex

Abstract Rationale. Reference ranges for tests of pulmonary function have been defined upon their distribution in cross-sectional samples of “healthy” participants since the mid-Nineteenth Century. Currently, “healthy” participants are defined as participants recruited from the general population without respiratory symptoms or diseases and without a smoking history. However, reporting of all these factors is known to be imprecise. To better understand the variability of this approach, we investigated the stability of a “healthy” sample over 12 years in a prospective, population-based cohort study. Methods. The Multi-Ethnic Study of Atherosclerosis (MESA) recruited a multiethnic sample of participants ages 45-84 free of clinical cardiovascular disease in 2000-02. The MESA Lung Study performed standardized spirometry in 2004-07, 2010-11, and 2016-18. We defined subpopulations of healthy normal never-smoking participants at each exam in which spirometry was measured according to: self-reported history of never smoking; self-reported absence of respiratory diseases; and self-reported absence of respiratory symptoms (wheezing, persistent cough, phlegm production, and breathless walking on level ground). Stability of the sample over time was assessed with the Kappa statistic. For each exam, we used the healthy never-smoking group to derive sex-stratified reference equations for pre-bronchodilator forced expiratory volume in 1 second (FEV1) by linear regression with predictors of age and height2. Lower limits of normal (LLN) FEV1 were calculated as predicted FEV1-1.645[asterisk]standard error of the estimate, with age=70 years and height=158 cm and 172 cm for female and male, respectively. Results. There were 1,101 (26%) healthy normals of 4,272 participants at baseline (28% White, 20% Black, 22% Hispanic, 30% Asian), 1,075 (34%) of 3,200 at first follow-up, and 614 (24%) of 2,597 at second follow-up. Of 1,863 participants who participated in all three exams, 299 were labeled healthy normal in all exams, 278 were healthy normal in two of three exams, and 188 were healthy normal in only one exam (κ=0.60 across the three exams). Of the 1,129 who reported no symptoms at second follow-up, 32% had reported at least one symptom in at least one previous exam. The different healthy normal groups led to estimates of LLN FEV1 that varied across exams by 60-90 mL (female: 1,356-1,419mL, SD=34; male: 1,963-2,053mL, SD=29). Conclusions. The healthy normal never-smoking sample in a longitudinal cohort study was only moderately stable over time, with modest implications for the estimation of the LLN. These findings raise further questions about the current, cross-sectional approach to the definition of normal pulmonary function.

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)0.030
metaresearch head score (Gemma)0.049
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 score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.062
GPT teacher head0.418
Teacher spread0.356 · 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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