Computed tomography total airway count predicts progression to COPD in at-risk smokers
Bibliographic record
Abstract
There is limited understanding of how to identify people at high risk of developing COPD. Our objective was to investigate the association between computed tomography (CT) total airway count (TAC) and incident COPD over 3 years among ever-smokers from the population-based Canadian Cohort Obstructive Lung Disease (CanCOLD) study. CT and spirometry were acquired in ever-smokers at baseline; spirometry was repeated at 3-year follow-up. CT TAC was generated by summing all airway segments in the segmented airway tree (VIDA Diagnostics, Inc.). CT airway wall area, wall thickness for a theoretical airway with 10 mm perimeter (Pi10), and low attenuation areas below −856 HU (LAA856) were also measured. Logistic and mixed effects regression models were constructed to determine the association for CT measurements with development of COPD and forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) decline, respectively. Among 316 at-risk participants evaluated at baseline (65±9 years, 40% female, 18±19 pack-years), incident COPD was detected in 56 participants (18%) over a median 3.1±0.6 years of follow-up. Among CT measurements, only TAC was associated with incident COPD (p=0.03), where a 1-sddecrement in TAC increased the odds ratio for incident COPD by a factor of two. In a multivariable linear regression model, reduced TAC was significantly associated with greater longitudinal FEV1/FVC decline (p=0.03), but no other measurements were significant. CT TAC predicts incident COPD in at-risk smokers, indicating that smokers exhibit early structural changes associated with COPD prior to abnormal spirometry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".