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Record W2157834188 · doi:10.1148/radiol.2015150037

COPD: Do Imaging Measurements of Emphysema and Airway Disease Explain Symptoms and Exercise Capacity?

2015· article· en· W2157834188 on OpenAlexafffund
Miranda Kirby, Damien Pike, Don D. Sin, Harvey O. Coxson, David G. McCormack, Grace Párraga

Bibliographic record

VenueRadiology · 2015
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsWestern UniversitySt. Paul's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineCOPDSpirometryAirwayAir trappingLung volumesMagnetic resonance imagingObstructive lung diseaseDiffusing capacityRadiologyPlethysmographLungInternal medicineCardiologySurgeryAsthma

Abstract

fetched live from OpenAlex

PURPOSE: To determine the role of imaging measurements of emphysema and airway disease in determining chronic obstructive pulmonary disease (COPD) symptoms and exercise limitation in patients with COPD, particularly in patients with mild-to-moderate disease. MATERIALS AND METHODS: Participants (n = 116) with Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade U (unclassified) or grade I-IV COPD provided informed consent to an ethics board-approved HIPAA-compliant protocol and underwent spirometry and plethysmography, completed the St George's Respiratory Questionnaire (SGRQ), completed a 6-minute walk test for the 6-minute walk distance (6MWD), and underwent hyperpolarized helium 3 ((3)He) magnetic resonance (MR) imaging and computed tomography (CT). Emphysema was estimated by using the MR imaging apparent diffusion coefficient (ADC) and the relative area of the CT attenuation histogram with attenuation of -950 HU or less (RA950). Airway disease was measured by using the CT airway wall thickness of airways with an internal perimeter of 10 mm and total airway count. Ventilation defect percentage at (3)He MR imaging was used to measure ventilation. Multivariable regression models for the 6MWD and SGRQ symptom subscore were used to evaluate the relationships between physiologic and imaging measurements. RESULTS: Multivariate modeling for the 6MWD in 80 patients with GOLD grade U-II COPD showed that ADC (β = 0.34, P = .04), diffusing capacity of the lung for carbon monoxide (β = 0.60, P = .0008), and residual volume/total lung capacity (β = -0.26, P = .02) were significant variables, while forced expiratory volume in 1 second (FEV1) and airway disease measurements were not. In 36 patients with GOLD grade III or IV disease, FEV1 (β = 0.48, P = .01) was the only significant contributor in a multivariate model for 6MWD. MR imaging emphysema measurements also made the greatest relative contribution to symptoms in patients with milder (GOLD grade U-II) COPD (ADC: β = 0.60, P = .005; RA950: β = -0.52, P = .02; FEV1: β = -0.45, P = .0002) and in grade III or IV disease (ADC: β = 0.95, P = .01; RA950: β = -0.62, P = .07; airway count: β = -0.49, P = .01). CONCLUSION: In patients with mild-to-moderate COPD, MR imaging emphysema measurements played a dominant role in the expression of exercise limitation, while both CT and MR imaging measurements of emphysema explained symptoms.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.036
GPT teacher head0.292
Teacher spread0.256 · 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

Citations41
Published2015
Admission routes2
Has abstractyes

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