Rivaroxaban and dabigatran did not affect clotting profiles in plasma reconstituted with varying levels of autologous platelets to the same degree as heparin when evaluated using thromboelastography
Bibliographic record
Abstract
OBJECTIVE: Current recommendations for treating patients with thromboembolism and concomitant thrombocytopenia are based on anecdotal data and expert opinion, rather than clinical studies. Our aim was to use an in-vitro model employing thromboelastography (TEG) to evaluate clot formation as a surrogate indicator of clinical tendency to hemorrhage, and investigate the interactions of plasma at varying concentrations of platelets in the presence of anticoagulants. METHODS: Platelet-rich and platelet-poor plasma isolated from whole blood were mixed together to obtain platelet concentrations ranging from less than 10-150 × 10 platelets/l. Clotting was initiated with tissue factor and measured by TEG. RESULTS: Different tissue factor concentrations were required to model clinical clotting profiles for plasma that contained heparin (UFH), low molecular weight heparin (LMWH), or fondaparinux. No tissue factor was required for rivaroxaban or dabigatran-clotting reactions. The time to initiate coagulation (R) was significantly delayed at platelet concentrations less than 30 × 10/l for UFH and LMWH, less than 20 × 10/l for fondaparinux, and less than 10 × 10/l for rivaroxaban and dabigatran. The strength of the clot was significantly compromised at all platelet concentrations in the presence of UFH, LMWH or fondaparinux. In contrast, rivaroxaban and dabigatran compromised clot strength at platelet concentrations less than 10 × 10/l. CONCLUSION: All anticoagulants tested compromised coagulation at specific platelet concentration thresholds. Rivaroxaban and dabigatran had reduced impact on clot formation at low-platelet concentrations compared with heparinoids, suggesting that the factor-specific inhibitors may be more favorable than traditional heparin-based treatment of thromboembolism in the presence of thrombocytopenia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".