Sex‐related differences in pulmonary vascular volume distribution
Bibliographic record
Abstract
Abstract Pulmonary arterial hypertension affects females more frequently than males, and there are known sex‐related differences in the lungs. However, normal sex‐related differences in pulmonary vascular structure remain incompletely described. We aimed to contrast computed tomography‐derived pulmonary vascular volume and its distribution within the lungs of healthy adult females and males. From the CanCOLD Study, we retrospectively identified healthy never‐smokers. We analyzed full‐inspiration computed tomography images, using vessel and airway segmentation to generate pulmonary vessel volume, vessel counts, and airway counts. Vessels were classified by cross‐sectional area >10, 5–10, and <5 mm 2 into bins, with volume summed within each area bin and in total. We included 46 females and 36 males (62 ± 9 years old). Females had lower total lung volume, total airway counts, total vessel counts, and total vessel volume (117 ± 31 vs. 164 ± 28 mL) versus males (all p < 0.001). Females also had lower vessel volume >10 mm 2 (14 ± 8 vs. 27 ± 9 mL), vessel volume 5–10 mm 2 (35 ± 11 vs. 55 ± 10 mL), and vessel volume <5 mm 2 (68 ± 18 vs. 82 ± 19 mL) (all p < 0.001). Normalized to total vessel volume, vessel volume >10 mm 2 (11 ± 4 vs. 16 ± 4%, p < 0.001) and 5–10 mm 2 (30 ± 6 vs. 34 ± 5%, p = 0.001) remained lower in females but vessel volume <5 mm 2 relative to total volume was 18% higher (59 ± 8 vs. 50 ± 7%, p < 0.001). Among healthy older adults, pulmonary vessel volume is distributed into smaller vessels in females versus males.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".