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Record W2753087220 · doi:10.1164/rccm.201704-0692oc

Total Airway Count on Computed Tomography and the Risk of Chronic Obstructive Pulmonary Disease Progression. Findings from a Population-based Study

2017· article· en· W2753087220 on OpenAlexafffundabout
Miranda Kirby, Naoya Tanabe, Wan C. Tan, Guohai Zhou, Ma’en Obeidat, Cameron Hague, Jonathon Leipsic, Jean Bourbeau, Don D. Sin, James C. Hogg, Harvey O. Coxson

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityRoyal Victoria HospitalMcGill University Health CentreAstraZeneca (Canada)St. Paul's HospitalCanadian Respiratory Research NetworkBoehringer Ingelheim (Canada)GlaxoSmithKline (Canada)University of British Columbia
FundersUniversity of TorontoQueen's UniversityMcGill UniversityUniversity of Ottawa
KeywordsMedicineCOPDSpirometryHounsfield scaleObstructive lung diseaseAirwayPopulationInternal medicineLungPulmonary function testingRespiratory diseaseCardiologyRadiologyComputed tomographySurgeryAsthma

Abstract

fetched live from OpenAlex

Abstract Rationale Studies of excised lungs show that significant airway attrition in the “quiet” zone occurs early in chronic obstructive pulmonary disease (COPD). Objectives To determine if the total number of airways quantified in vivo using computed tomography (CT) reflects early airway-related disease changes and is associated with lung function decline independent of emphysema in COPD. Methods Participants in the multicenter, population-based, longitudinal CanCOLD (Canadian Chronic Obstructive Lung Disease) study underwent inspiratory/expiratory CT at visit 1; spirometry was performed at four visits over 6 years. Emphysema was quantified as the CT inspiratory low-attenuation areas below −950 Hounsfield units. CT total airway count (TAC) was measured as well as airway inner diameter and wall area using anatomically equivalent airways. Measurements and Main Results Participants included never-smokers (n = 286), smokers with normal spirometry at risk for COPD (n = 298), Global Initiative for Chronic Obstructive Lung Disease (GOLD) I COPD (n = 361), and GOLD II COPD (n = 239). TAC was significantly reduced by 19% in both GOLD I and GOLD II compared with never-smokers (P < 0.0001) and by 17% in both GOLD I and GOLD II compared with at-risk participants (P < 0.0001) after adjusting for low-attenuation areas below −950 Hounsfield units. Further analysis revealed parent airways with missing daughter branches had reduced inner diameters (P < 0.0001) and thinner walls (P < 0.0001) compared with those without missing daughter branches. Among all CT measures, TAC had the greatest influence on FEV1 (P < 0.0001), FEV1/FVC (P < 0.0001), and bronchodilator responsiveness (P < 0.0001). TAC was independently associated with lung function decline (FEV1, P = 0.02; FEV1/FVC, P = 0.01). Conclusions TAC may reflect the airway-related disease changes that accumulate in the “quiet” zone in early/mild COPD, indicating that TAC acquired with commercially available software across various CT platforms may be a biomarker to predict accelerated COPD progression.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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".

Quick stats

Citations241
Published2017
Admission routes3
Has abstractyes

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