Thromboelastography to Monitor Clotting/Bleeding Complications in Patients Treated with the Molecular Adsorbent Recirculating System
Bibliographic record
Abstract
Background. The Molecular Adsorbent Recirculating System (MARS) has been shown to clear albumin-bound toxins from patients with liver failure but might cause bleeding complications potentially obscuring survival benefits. We hypothesized that monitoring clotting parameters and bed-side thromboelastography allows to reduce bleeding complications. Methods. Retrospective analysis of 25 MARS sessions during which clotting parameters were monitored by a standardized protocol. Results. During MARS therapy median INR increased significantly from 1.7 to 1.9 platelet count and fibrinogen content decreased significantly from 57 fL(-1) to 42 fL(-1) and 2.1 g/L to 1.5 g/L. Nine relevant complications occurred: the MARS system clotted 6 times 3 times we observed hemorrhages. Absent thrombocytopenia and elevated plasma fibrinogen predicted clotting of the MARS system (ROC 0.94 and 0.82). Fibrinolysis, detected by thromboelastography, uniquely predicted bleeding events. Conclusion. Bed-side thromboelastography and close monitoring of coagulation parameters can predict and, therefore, help prevent bleeding complications during MARS therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".