Sonorheometry Device Thresholds in Liver Transplantation: An Observational Retrospective Study
Bibliographic record
Abstract
Background: Liver transplantation (LT) remains a potentially haemorrhagic procedure whose perioperative bleeding and transfusion could be better monitored using point-of-care devices. Quantra® is a device based on sonorheometry to assess whole blood clot formation. Our aims were to describe Quantra® parameters during LT and to study their correlations with standard laboratory parameters, and to determine Quantra® cut-off values for thrombocytopenia, hypofibrinogenemia and coagulation factors’ deficit. Methods: In 34 patients undergoing LT, blood samples were collected before surgical incision, 15 min after the beginning of the anhepatic phase, and 15 min after arterial revascularization of the graft. Results: Clotting time (CT) was well correlated with prothrombin (PT) ratio and activated partial thromboplastin time (aPTT) ratio. Platelet contribution to clot stiffness (PCS) was correlated with platelets (ρ = 0.82, p < 0.001) and fibrinogen contribution clot stiffness (FCS) with fibrinogen (Fg) (ρ = 0.74, p < 0.001). CT predicted a PT ratio < 30% with an area under the curve (AUC) of 0.93 (95% CI 0.87–0.98; p < 0.001). PCS predicted a platelet count < 50 G/L with an AUC of 0.87 (95% CI 0.76–0.98, p < 0.001). FCS predicted a Fg < 1.0, 1.2 or 1.5 g/L, with an AUC of 0.86 (95% CI 0.77–094, p < 0.001), 0.82 (95% CI 0.74–0.91, p < 0.001) and 0.88 (95% CI 0.82–0.95, p < 0.001), respectively. Conclusion: Quantra® provides a rapid assessment of haemostasis during LT.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".