Outcomes-based systematic review for management of massive intra-cardiac or pulmonary thrombotic emboli during surgery.
Bibliographic record
Abstract
INTRODUCTION: The management of massive intra-operative embolism remains controversial. Our hypothesis was that either surgical or medical thrombectomy offers survival benefit in these patients. METHODS: Published case reports were reviewed for intra-operative intra-cardiac or pulmonary embolism and outcomes for the following four intervention groups were evaluated for mortality benefit: surgical embolectomy; thrombolysis; anticoagulation; supportive care alone. We also assessed whether the use of diagnostic modalities prior to each embolism event resulted in a mortality benefit and, separately, whether post-intervention improvement in physiologic parameters resulted in improvement in outcomes. Univariate analyses and logistic regression were performed to assess the impact of the four primary interventions on mortality, the primary outcome. RESULTS: Seventy-eight cases were reviewed and therapeutic interventions resulted in improved survival (70%) compared to supportive care (45%), odds ratio=0.38[0.15-0.98], p=0.04. Univariate analysis of primary interventions with death as a primary outcome resulted in a lack of significantly different outcomes (p=0.08). Mortality rates were 71% in the thrombolytic; 28% in surgical embolectomy; 18% in anticoagulation and 43% in the supportive care groups. The routine pre-event use of trans-esophageal echocardiography was not related with improved outcomes (p=0.36) but the use of pulmonary artery or central venous catheters was (p=0.035). Post-intervention improvements in the physiologic parameters of each diagnostic modality were associated with an improvement in mortality (p<0.05). CONCLUSIONS: Our data present some important trends among the intervention groups, raising significant concerns about the safety for the use of thrombolytics in the management of intra-operative 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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".