Effect of anticoagulants on fibrin clot structure: A comparison between vitamin K antagonists and factor Xa inhibitors
Bibliographic record
Abstract
BACKGROUND: Abnormal clot structure has been identified in patients with thrombotic disorders. Anticoagulant therapy offers clear benefits for thrombosis prevention and treatment by reducing blood clot formation and size; nevertheless, there are limited data on the effects of different anticoagulants, where clotting is initiated with different triggers, on clot structure. OBJECTIVES: Our aim was to investigate the effects of vitamin K antagonists and factor Xa inhibitors on clot structure. METHODS: Clots from pooled plasma spiked with rivaroxaban, apixaban, or enoxaparin, as well as plasma from patients on warfarin, were compared to plasma without anticoagulation. The kinetic profile of polymerizing clots was obtained by turbidity, fiber density was determined by confocal microscopy, clot pore size was investigated by permeation, and fiber size was analyzed using scanning electron microscopy. Clotting agonist was either tissue factor or thrombin. RESULTS: Following clotting with tissue factor, all anticoagulated clots had a significantly increased lag time, with the exception of enoxaparin. Rivaroxaban additionally led to significantly less dense and more permeable clots, with thicker fibers. In contrast, turbidity analysis following initiation with thrombin showed few effects of anticoagulation, with only enoxaparin leading to a prolonged lag time. Enoxaparin clots made with thrombin were less dense and more permeable. CONCLUSION: Our results show that anticoagulants modulate clot structure particularly when induced by tissue factor, most likely due to reduction of thrombin generation. We propose that the effects of different anticoagulants could be assessed with a global clot structure measurement such as permeation or turbidity, providing information on clot phenotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".