Imaging techniques in chronic thromboembolic pulmonary hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic thromboembolic pulmonary hypertension (CTEPH) can affect up to 4-5% of patients with acute pulmonary embolism. It is likely an underdiagnosed entity. Misdiagnosis is common because patients often present with nonspecific symptoms of pulmonary hypertension. Early diagnosis may help improve the outcome, as CTEPH is potentially curable with pulmonary thromboendarterectomy (PEA). Imaging is central to an accurate diagnosis, and for assessing correctly the technical feasibility of PEA. This review examines the findings of various imaging techniques in CTEPH and their contribution in the diagnostic and therapeutic evaluation of the disease. RECENT FINDINGS: Ventilation-perfusion scintigraphy remains a sensitive method for excluding CTEPH. Multidetector computed tomography angiography (MDCTA) depicts directly changes of CTEPH, provides a surgical 'road map', and should be used for the diagnostic assessment of all suitable patients with pulmonary arterial hypertension. In many centers, the role of conventional pulmonary angiography is gradually being replaced by cross-sectional methods. MRI has a role in preoperative and postoperative assessment of right ventricular function and can depict vascular abnormalities up to segmental level. SUMMARY: MDCTA in combination with MRI represent the main techniques for the diagnosis and management of CTEPH. Newer techniques such as dual spectrum computed tomography may further improve preoperative and postoperative assessment of CTEPH patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".