Chronic Thromboembolic Pulmonary Arterial Hypertension
Bibliographic record
Abstract
INTRODUCTION: Pulmonary thromboendarterectomy is the treatment of choice for patients with chronic thromboembolic pulmonary arterial hypertension (CTEPH). Some patients do poorly after this procedure and may be better candidates for heart-lung transplant. The purpose of this study was to correlate preoperative findings on helical contrast-enhanced computed tomography (CT) with surgical outcome. METHODS: Thirty-seven patients (mean age 52.9, range 22-71) who underwent pulmonary thromboendarterectomy and had preoperative helical contrast-enhanced CT followed by High Resolution CT (HRCT) scans were included in the study. The CTs were evaluated for the presence of central and segmental disease and for the presence of mosaic perfusion pattern. RESULTS: The presence of central disease, as well as the presence of segmental disease, correlated negatively with the postoperative mean pulmonary arterial pressure [r(c) = -0.401, P = 0.015, r(s) = -0.38, P = 0.024)] and the pulmonary vascular resistance [(r(c) = -0.37, P = 0.027, r(s) = -0.39, P = 0.019]. No correlation was found between the clinical variables and the presence of mosaic perfusion pattern. CONCLUSION: Patients with CTEPH and evidence of chronic PE in the central or segmental pulmonary arteries have a better clinical outcome after pulmonary thromboendarterectomy than patients without these findings. The presence of mosaic perfusion pattern is not helpful in predicting postoperative outcome.
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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) | 0.007 | 0.001 |
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".