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Record W2997298847 · doi:10.1164/rccm.201908-1552oc

Is Computed Tomography Airway Count Related to Asthma Severity and Airway Structure and Function?

2020· article· en· W2997298847 on OpenAlexafffund
Rachel L. Eddy, Sarah Svenningsen, Miranda Kirby, Danielle Knipping, David G. McCormack, Christopher Licskai, Parameswaran Nair, Grace Párraga

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsWestern UniversityToronto Metropolitan UniversityMcMaster UniversitySt. Joseph’s Healthcare HamiltonRobarts Clinical Trials
FundersCanadian Institutes of Health Research
KeywordsMedicineAirwayAsthmaSpirometryBronchodilatorMagnetic resonance imagingLumen (anatomy)RadiologyInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Abstract Rationale In patients with asthma, X-ray computed tomography (CT) has provided evidence of thickened airway walls and airway occlusions, but the total number of CT-visible airways and its relationship with disease severity is unknown. Objectives To measure CT total airway count (TAC) in asthma and evaluate relationships with asthma severity, airway morphology, pulmonary function, and magnetic resonance imaging (MRI) ventilation. Methods Participants underwent post-bronchodilator inspiratory CT, and prebronchodilator and post-bronchodilator spirometry and hyperpolarized 3He MRI. CT TAC was quantified as the sum of airways in the segmented airway tree, and airway wall area percent (WA%) and lumen area were measured. MRI ventilation abnormalities were quantified as the ventilation defect percent. Measurements and Main Results We evaluated 70 participants, including 15 Global Initiative for Asthma (GINA) steps 1 to 3, 19 GINA 4, and 36 GINA 5 participants with asthma. As compared with GINA 1 to 3, TAC was significantly diminished in GINA 4 (P = 0.03) and GINA 5 (P = 0.045). Terminal airway intraluminal occlusion was present in 5 (2 GINA 4 and 3 GINA 5) of 70 participants. Sub-subsegmental airways were CT-invisible or missing in 69 out of 70 participants; the most common number of missing sub-subsegments was 10. Participants with ≥10 missing subsegments had worse WA% (P < 0.0001), lumen area (P < 0.0001), and ventilation defect percent (P = 0.03) than those with <10 missing subsegments. In a multivariable model, TAC (standardized regression coefficient = 0.50; P = 0.001) independently predicted FEV1 (R2 = 0.27; P = 0.003) and, in a separate model, TAC (standardized regression coefficient = −0.53; P < 0.0001) independently predicted airway WA% (R2 = 0.32; P = 0.0001). Conclusions TAC was significantly diminished in participants with greater asthma severity and was related to airway wall thickness and ventilation defects. Fewer airways in severe than in mild asthma challenges our understanding of airway disease in asthma. Clinical trial registered with www.clinicaltrials.gov (NCT02351141).

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)0.002
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.297
Teacher spread0.284 · 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

Citations81
Published2020
Admission routes2
Has abstractyes

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