Computed tomography identifies sex-specific differences in surgical chronic thromboembolic pulmonary hypertension
Bibliographic record
Abstract
Background Registry data suggest women are less likely than men to undergo pulmonary thromboendarterectomy for chronic thromboembolic pulmonary hypertension despite a similar proportion of proximal vs distal disease. We hypothesized that sex-specific differences could be elicited with a computed tomography pulmonary angiography analysis beyond proximal vs distal. Methods Preoperative computed tomography pulmonary angiography of patients who underwent pulmonary thromboendarterectomy for chronic thromboembolic pulmonary hypertension from January 2017 to September 2021 was analyzed. The pulmonary vascular tree was divided into 32 named vessels with chronic thromboembolism presence and lesion type recorded for each vessel. If no lesion was identified in a segmental vessel, subsegmental disease was recorded when present. Results One hundred forty-four patients (mean age 57 ± 15 years, 78 women) were included. There were no sex differences in baseline hemodynamics. Men had more vessels involved than women (mean 20.3 vs 17.1, p = 0.004) and had fewer disease-free pulmonary segments (mean 4.9 ± 4.3 vs 7.6 ± 5.5, p = 0.001). Men had a greater number of webs, eccentric thickening, and occlusions. The distribution of lesion type did not significantly differ between sexes at the main or lobar level but men had significantly more lesions in the segmental vasculature while women had a higher proportion of subsegmental lesions ( p < 0.001). Conclusions Sex-specific differences in chronic thromboembolic pulmonary hypertension are demonstrated on computed tomography pulmonary angiography in overall distribution and lesion type at the segmental and subsegmental level with women having fewer and more distal lesions despite similar hemodynamics.
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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 | 0.003 |
| 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 | 0.000 | 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".