Preventing and managing catastrophic bleeding during extracorporeal circulation
Bibliographic record
Abstract
The use of extracorporeal circulation for cardiac surgery and extracorporeal life support poses tremendous challenges to the hemostatic equilibrium given its diametric tendency to trigger hyper- and hypocoagulopathy. The necessity of anticoagulant therapy to counteract the hemostatic activation by the extracorporeal circuitry compounded by unfavorable patient and surgical factors significantly increase the risk of catastrophic bleeding in patients who require extracorporeal circulation. Preoperative measures, such as stratification of high-risk bleeding patients, and optimization of the modifiable variables, including anemia and thrombocytopenia, provide a crude estimation of the likelihood and may modify the risk of catastrophic bleeding. The anticipation for catastrophic bleeding subsequently prompts the appropriate preparation for potential resuscitation and massive transfusion. Equally important is intraoperative prevention with the prophylactic application of tranexamic acid, an antifibrinolytic agent that has promising benefits in reduction of blood loss and transfusion. In the event of uncontrolled catastrophic bleeding despite preemptive strategies, all effort must be centered on regaining hemostasis through surgical control and damage control resuscitation to protect against worsening coagulopathy and end organ failure. When control of bleeding is reinstated, management should shift focus from systemic therapy to targeted hemostatic therapy aimed at the potential culprits of coagulopathy as identified by point of care hemostatic testing. This review article outlines the strategies to appropriately intervene using prediction, prevention, preparation, protection, and promotion of hemostasis in managing catastrophic bleeding in extracorporeal circulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".