The Role of Mechanical Thrombectomy in High-Risk Pulmonary Emboli
Bibliographic record
Abstract
Background: Pulmonary emboli (PE) are obstructions in the pulmonary arteries causing reduced alveolar perfusion. They remain a significant treatment challenge in acute medicine. They are a significant cause of patient mortality, particularly high-risk PE, which can present with hemodynamic instability and have documented 30-day mortality rates as high as 25 percent. Therefore, it is imperative to be able to promptly recognize and treat patients with high-risk PE. The treatment landscape of PE has evolved significantly, and continues to do so, especially given the advancement of catheter-directed therapies. Herein, we explore the current best practices for the diagnosis and treatment of high-risk PE, with a focus on pathophysiology, risk stratification, and the latest advancements in diagnosis and treatment. Additionally, we discuss the comparative evidence between treatment strategies in high-risk PE, including the role of mechanical thrombectomy devices. Educational Points: (1) Highlight the classification and pathophysiology of pulmonary emboli, and the importance of identifying patients at high risk of clinical deterioration. (2) Discuss the diagnostic approaches to high-risk pulmonary emboli and the appropriate selection of imaging studies. (3) Review and understand the current best practices in the treatment of high-risk pulmonary embolism. (4) Reviewing the literature base in the treatment of high-risk pulmonary emboli, and identifying areas requiring further research. Publication History Article published online: 02 April 2024 © 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) Georg Thieme Verlag KG Stuttgart · New York
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".