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Record W71478886 · doi:10.5588/ijtld.12.0054

Restricted spirometry in the Burden of Lung Disease Study.

2012· article· en· W71478886 on OpenAlexaff
David M. Mannino, Mary Ann McBurnie, Wan C. Tan, Ali Kocabaş, J. M. Antó, William M. Vollmer, A. Sonia Buist

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpirometryMedicineObstructive lung diseaseVital capacityDiabetes mellitusPopulationLung volumesDiseasePhysical therapyInternal medicineLung diseaseCross-sectional studyLungLung functionCardiologyAsthmaCOPDEnvironmental healthPathologyDiffusing capacityEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: The presence of restrictive lung disease has classically required the measure of total lung capacity to document 'true' restriction, which has limited its detection in large population-based studies. METHODS: We used spirometric data to classify people with restricted spirometry (forced expiratory volume in 1 second [FEV(1)]/forced vital capacity ≥ 0.70 and FEV(1) < 80% predicted) in the Burden of Lung Disease (BOLD) Study and determined the relation between this finding and demographic factors and the presence of chronic diseases, including diabetes mellitus, hypertension and cardiovascular disease. RESULTS: Overall, we found that 11.7% of men (546/4664) and 16.4% of women (836/5098) had restricted spirometry. Prevalence varied widely by site, from a low of 4.2% among males in Sydney, Australia, to a high of 48.7% among females in Manila, The Philippines. Compared to people with normal lung function, those with restricted spirometry had a higher prevalence of diabetes (12.2% vs. 4.6%), heart disease (15.0% vs. 7.7%) and hypertension (38.8% vs. 22.8%). CONCLUSIONS: Restricted spirometry is a common finding in population studies. Additional research is needed to better define and describe the mechanisms that lead to restricted spirometry and potential interventions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

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

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 head0.031
GPT teacher head0.301
Teacher spread0.269 · 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 teacher head, 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".

Quick stats

Citations107
Published2012
Admission routes1
Has abstractyes

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