Role of extracorporeal membrane oxygenation and surgical embolectomy in acute pulmonary embolism
Bibliographic record
Abstract
PURPOSE OF REVIEW: Surgery is an important option to consider in patients with massive and submassive pulmonary emboli. Earlier intervention, better patient selection, improved surgical techniques and the use of veno-arterial extracorporeal membrane oxygenation (VA ECMO) have contributed to improve the safety of surgery for pulmonary emboli. RECENT FINDINGS: VA ECMO is rapidly changing the initial management of patients with massive pulmonary emboli, providing an opportunity for stabilization and optimization before intervention. The early and long-term consequences of acute pulmonary emboli are better understood, in particular with regard to the risks of chronic thromboembolic pulmonary hypertension (CTEPH), an entity that should be identified in the acute setting as much as possible. The presence of chronic thromboembolic pulmonary disease can be associated with persistent haemodynamic instability despite removal of the acute thrombi, particularly if pulmonary hypertension is established. The pulmonary embolism response team (PERT) is an important component in the management of massive and submassive acute pulmonary emboli to determine the best treatment options for each patient depending on their clinical presentation. SUMMARY: Three types of surgery can be performed for pulmonary emboli depending on the extent and degree of organization of the thrombi (pulmonary embolectomy, pulmonary thrombo-embolectomy and pulmonary thrombo-endarterectomy). Other treatment options in the context of acute pulmonary emboli include thrombolysis and catheter-directed embolectomy. Future research should determine how best to integrate VA ECMO as a bridging strategy to recovery or intervention in the treatment algorithm of patients with acute massive pulmonary emboli.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".