Fractal analysis of pulmonary vasculature in COPD: Association with pulmonary function, functional capacity and symptoms
Bibliographic record
Abstract
Background: The pulmonary vasculature undergoes remodeling and pruning in chronic obstructive pulmonary disease (COPD). While existing computed tomography (CT) imaging measures quantify vascular pruning, advanced methods can capture greater complexity of the structural changes. Aim: Investigate the association of CT vascular fractal dimension (VFD) measurements with pulmonary function, functional capacity and symptoms, independent of existing CT vessel pruning measures, in COPD. Methods: CanCOLD participants underwent CT imaging and analysis (VIDA Diagnostics). The relative volume of the vessels <5mm in cross-sectional area to the total blood volume (BV5/TBV) was generated. Segmented vasculature complexity was measured by VFD using Minkowski-Bouligand box-counting dimension. Fully adjusted linear regression models for pulmonary function, exercise limitation (6-minute walk test, 6MWT) and symptom burden (Medical Research Council Dyspnoea Scale, MRC) with VFD and BV5/TBV were performed in both separate and the same models. Results: 1325 CanCOLD participants were analyzed: n=279 never-smokers, n=405 at risk, n=366 GOLD I, and n=275 GOLD II+ COPD. In separate multivariable regression models, VFD measurements were associated with reduced FEV1 (p<0.05) and 6MWD (p<0.05), and increased MRC scores (p<0.05). In the same model as BV5/TBV, only VFD was associated with reduced 6MWD (p<0.05) and increased MRC scores (p<0.05). Conclusion: CT vascular fractal dimension measure captures greater complexity of vessel remodeling and is independently associated with respiratory morbidity in COPD, providing complementary information to existing vascular pruning measures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".