Platelets and coagulation in infection
Bibliographic record
Abstract
Disseminated intravascular coagulation (DIC) is a frequent complication in sepsis that is associated with worse outcomes and higher mortality in patients. In addition to the uncontrolled generation of thrombi throughout the patient's vasculature, DIC often consumes large quantities of clotting factors leaving the patient susceptible to hemorrhaging. Owing to these complications, patients often receive anticoagulants to treat the uncontrolled clotting, often with mixed outcomes. This lack of success with the current array of anticoagulants can be partly explained by the fact that during sepsis clotting is often initiated by the immune system. Systemic inflammation has the capacity to activate and amplify coagulation and, as such, potential therapies for the treatment of sepsis-associated DIC need to address the interaction between inflammation and coagulation. Recent studies have suggested that platelets and neutrophil extracellular traps (NETs) are the key mediators of infection-induced coagulation. This review explores current anticoagulant therapies and discusses the development of future therapies to target platelet and NET-mediated coagulation.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".