Advance in the Management of Sepsis-Induced Coagulopathy and Disseminated Intravascular Coagulation
Bibliographic record
Abstract
Coagulopathy commonly occurs in sepsis as a critical host response to infection that can progress to disseminated intravascular coagulation (DIC) with an increased mortality. Recent studies have further defined factors responsible for the thromboinflammatory response and intravascular thrombosis, including neutrophil extracellular traps, extracellular vesicles, damage-associated molecular patterns, and endothelial glycocalyx shedding. Diagnosing DIC facilitates sepsis management, and is associated with improved outcomes. Although the International Society on Thrombosis and Haemostasis (ISTH) has proposed criteria for diagnosing overt DIC, these criteria are not suitable for early detection. Accordingly, the ISTH DIC Scientific Standardization Committee has proposed a new category termed "sepsis-induced coagulopathy (SIC)" to facilitate earlier diagnosis of DIC and potentially more rapid interventions in these critically ill patients. Therapy of SIC includes both treatment of the underlying infection and correcting the coagulopathy, with most therapeutic approaches focusing on anticoagulant therapy. Recently, a phase III trial of recombinant thrombomodulin was performed in coagulopathic patients. Although the 28-day mortality was improved by 2.6% (absolute difference), it did not reach statistical significance. However, in patients who met entry criteria for SIC at baseline, the mortality difference was approximately 5% without increased risk of bleeding. In this review, we discuss current advances in managing SIC and DIC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".