Artificial liver support with the molecular adsorbent recirculating system: activation of coagulation and bleeding complications
Bibliographic record
Abstract
BACKGROUND: Numerous, mostly uncontrolled, observations suggest that artificial liver support with the Molecular Adsorbent Recirculating System (MARS) improves pathophysiologic sequelae and outcome of acute and acute-on-chronic liver failure. MARS is felt to be safe, but extracorporeal circuits may activate coagulation. OBJECTIVE: To assess the frequency of and risk factors for activation of coagulation during MARS treatment. PATIENTS/METHODS: Retrospective analysis of coagulopathy/bleeding complications observed during 83 consecutive MARS sessions in 21 patients (11 men; median age 46 years; median three sessions per patient; median duration of session 8 h). RESULTS: Nine clinically relevant episodes of coagulopathy/bleeding were observed in eight patients, forced to premature cessation of MARS in seven and ended lethal in four. Four complications occurred during the first, five during later (third to seventh) MARS sessions and two bleeders tolerated further sessions without complications. Coagulation parameters worsened significantly also during MARS sessions not associated with bleeding (P< or =0.004). In univariate analysis, patient's age, vasopressor therapy, pretreatment INR, fibrin D-dimer and fibrinogen concentrations, but not severity of underlying disease (MELD, Child-Pugh, SAPS II scores), were significantly associated with coagulopathy (P<0.05). Only patient's age, fibrin D-dimer level and INR were retained in a multivariate model correctly classifying 98% of sessions without, but only 33% with complications. CONCLUSION: Coagulation is frequently activated during MARS therapy, potentially leading to bleeding complications and mortality.
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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 | 0.003 |
| 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".