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Record W4392954043 · doi:10.1093/radadv/umae002

CT trachea surface roughness is associated with chronic obstructive pulmonary disease symptoms

2024· article· en· W4392954043 on OpenAlexafffundabout
J. Bartlett, James C. Hogg, Jean Bourbeau, Wan C. Tan, Miranda Kirby

Bibliographic record

VenueRadiology Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsRoyal Victoria HospitalMcGill University Health CentreUniversity of British ColumbiaToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéPfizer CanadaNovartis Pharmaceuticals CanadaReseau canadien de recherche respiratoireDalhousie UniversityCanadian Lung AssociationNovartis PharmaBritish Columbia Lung AssociationJohns Hopkins UniversityCanadian Thoracic SocietyMcGill UniversityUniversity of OttawaQueen's UniversityPfizerUniversity of TorontoGlaxoSmithKline
KeywordsPulmonary diseaseMedicineCardiologySurface roughnessInternal medicineSurface finishRadiologyMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Background Trachea structural abnormalities occur in patients with chronic obstructive pulmonary disease (COPD), yet there are few methods for quantifying trachea surface topology. Purpose To develop a method to quantify trachea surface roughness on CT imaging and investigate the association with airflow limitation and symptoms in COPD. Materials and Methods Participants from the multicenter prospective Canadian Cohort Obstructive Lung Disease study between 2009 and 2015 underwent CT imaging and analysis. Established CT measurements included: tracheal index (TI), defined as the smallest ratio of coronal-to-sagittal trachea diameter, low attenuation areas below –950 HU, and wall thickness of a theoretical 10-mm airway. Trachea surface roughness shape (SRS) was calculated as the percent fraction of the measurement box filled by the surface mesh. Multivariable regression models were used to determine association for CT measurements with forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC), and Medical Research Council dyspnea scale (MRC)≥3, adjusting for covariates. Results A total of 1253 participants (mean age, 66 ± 10 years; 727 men) from 9 centers were investigated: n = 267 never smokers, n = 369 ever smokers, n = 352 mild COPD, and n = 265 moderate-to-severe COPD. There were no differences between groups for age or race (P < .05). In models including SRS and TI, a 1-standard deviation (SD) increase in SRS was independently associated with a 0.11-SD decrease in FEV1 (β = –0.11; P < .001) and a 0.16-SD decrease in FEV1/FVC (β = –0.16; P < .001); a 1-point increase in SRS was associated with a 13% increased likelihood of MRC ≥ 3 (odds ratio = 1.13; P = .003). In models including SRS, low attenuation areas below –950 HU and wall thickness of a theoretical 10-mm airway, a 1-SD increase in SRS was associated with a 0.21-SD decrease in FEV1 (β = –0.21; P < .001) and a 0.13-SD decrease in FEV1/FVC (β = –0.13; P < .001); a 1-point increase in SRS was associated with a 12% increased likelihood of MRC ≥ 3 (odds ratio = 1.12; P = .006). Conclusion Increased trachea surface shape roughness is independently associated with worse airflow and increased symptom burden in COPD.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.259
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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