Computed tomography total vessel count and vessel count percentages: associations with exercise capacity, symptoms and rapid FEV <sub>1</sub> decline in COPD
Bibliographic record
Abstract
Background Existing computed tomography (CT) vascular pruning measures rely on volumes, such as the proportion of blood volume in vessels with cross-sectional area (CSA) ≤5 mm 2 or CSA ≤10 mm 2 /total blood vessel volume (BV5/TBV or BV10/TBV, respectively), but may underestimate vascular pruning due to blood redistribution to larger vessels in individuals with milder COPD. We aim to develop a novel CT vessel measure, the total vessel count (TVC), quantify small and combined small/intermediate vessel count percentages, and compare these measures with BV5/TBV and BV10/TBV in terms of associations with lung function and its decline. As a secondary aim, associations with exercise capacity and COPD symptoms will be investigated. Methods CanCOLD (Canadian Cohort Obstructive Lung Disease) participants underwent CT imaging. BV5/TBV and BV10/TBV were generated using vessel segmentation. TVC and small (CSA ≤5 mm 2 , VC ≤5 /TVC) and combined small/intermediate (CSA ≤10 mm 2 , VC ≤10 /TVC) vessel count percentages were calculated. Fully adjusted regression models assessed associations with forced expiratory volume in 1 s (FEV 1 ), FEV 1 /forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide ( D LCO ), accelerated FEV 1 decline over 3 years, peak oxygen uptake ( V̇ O 2 peak ), 6-min walk distance (6MWD) and Medical Research Council (MRC) dyspnoea scale. Results 1254 CanCOLD participants were investigated. TVC, VC ≤5 /TVC and VC ≤10 /TVC were associated with FEV 1 /FVC (p<0.05), D LCO (p<0.05) and FEV 1 decline (p<0.05); VC ≤10 /TVC was associated with V̇ O 2 peak (p<0.05), and both VC ≤5 /TVC and VC ≤10 /TVC were associated with MRC (p<0.05). BV5/TBV and BV10/TBV were only associated with 6MWD (p<0.05) and MRC (p<0.05). Conclusion Pulmonary vessel count percent, a measure of vasculature narrowing/loss, is associated with lung function and its decline, reduced exercise capacity, and increased symptoms in COPD.
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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) | 0.002 | 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".