Role of rheolytic thrombectomy in massive pulmonary embolism with contraindication to systemic thrombolytic therapy
Bibliographic record
Abstract
AIMS: Mortality of massive pulmonary embolism remains exceedingly high despite thrombolytic therapy. Despite initial encouraging results, rheolytic thrombectomy has not been considered the first choice of treatment in the current European Guidelines for massive pulmonary embolism, even in cases of major contraindication to thrombolysis. Our objective was to assess the efficacy of rheolytic thrombectomy in the specific treatment of massive pulmonary embolism with contraindication to systemic thrombolytic therapy. METHODS AND RESULTS: Between January 2003 and April 2008 a total of 10 patients with massive pulmonary embolism referred for rheolytic thrombectomy were included. Clinical data including medical history, haemodynamic status, procedural characteristic, in-hospital complications and survival were collected. Seven patients survived after undergoing the procedure, three patients died in during their initial hospitalisation however, two of these deaths were not attributable to the pulmonary embolism or the procedure. Rheolytic thrombectomy resulted in reduction of mean pulmonary artery pressures from 34.6+/-13.1 mmHg to 26.9+/-8.2 mmHg immediately following the procedure. Additionally, the Miller index improved from 22.4+/-2.8 to 9.8+/-2.7. There were no periprocedural bleeding complications associated with the procedure. CONCLUSIONS: Rheolytic thrombectomy might be an effective and safe treatment for massive pulmonary embolism when systemic thrombolytic therapy is contraindicated. These data form the basis for further clinical investigation of this novel therapy among patients with massive pulmonary embolism.
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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 | 0.005 |
| 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) | 0.001 | 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".