Fibrinolysis during cardiopulmonary bypass detected with thromboelastography
Bibliographic record
Abstract
Fibrinolysis is a common haemostatic abnormality during cardiopulmonary bypass (CPB). Thromboelastography (TEG) is a good method to detect both types of fibrinolysis. Four hundred and ninety-nine patients during mild hypothermic CPB and elective surgery were monitored with TEG (first – after the induction, second – after rewarming, third and fourth – at the end of surgery native and heparinase). No prophylactic antifibrinolytics were used. The data of the study group were compared with a control group of 475 patients monitored only with laboratory tests (fibrin degradation products (FDP) and D-dimers). Peroperative and 24 hour postoperative bleeding, number of transfusions, aprotinin therapy and reexploration were recorded. Correlations between the presence of fibrinolysis and blood loss and transfusion therapy and between aprotinin administration and blood loss and number of transfusions were evaluated. The frequency of fibrinolysis measured with TEG: before surgery – primary 3.2%/secondary 3.4%; during CPB – 18.8%/0.6%; after surgery – 7%/1.4% (native), 5.6%/0.6% (heparinase). Positivity of fibrinolysis detected with laboratory tests was 100%. The TEG parameter of fibrinolysis (LY30) was significantly increased during CPB. The frequency of aprotinin administration was 12% TEG, 10.7% control. No correlation between positivity of fibrinolysis and peroperative/postoperative blood loss and red blood cells (RBC) and fresh frozen plasma (FFP) transfusions were recorded. No correlation between aprotinin administration and peroperative/postoperative blood loss and RBC transfusion were recorded. Positive correlation between aprotinin administration and FFP transfusion were recorded Fibrinolysis was usually not associated with serious bleeding. There was no positive effect of aprotinin to reduce bleeding or transfusion therapy. FDP and D-dimers are not useful to detect fibrinolysis in cardiac surgery.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".