Recent Advances in Anticoagulant Treatment of Immune Thrombosis: A Focus on Direct Oral Anticoagulants in Heparin-Induced Thrombocytopenia and Anti-Phospholipid Syndrome
Bibliographic record
Abstract
For more than 10 years, direct oral anticoagulants (DOACs) have been increasingly prescribed for the prevention and treatment of thrombotic events. However, their use in immunothrombotic disorders, namely heparin-induced thrombocytopenia (HIT) and antiphospholipid syndrome (APS), is still under investigation. The prothrombotic state resulting from the autoimmune mechanism, multicellular activation, and platelet count decrease, constitutes similarities between HIT and APS. Moreover, they both share the complexity of the biological diagnosis. Current treatment of HIT firstly relies on parenteral non-heparin therapies, but DOACs have been included in American and French guidelines for a few years, providing the advantage of limiting the need for treatment monitoring. In APS, vitamin K antagonists are conversely the main treatment (+/- anti-platelet agents), and the use of DOACs is either subject to precautionary recommendations or is not recommended in severe APS. While some randomized controlled trials have been conducted regarding the use of DOACs in APS, only retrospective studies have examined HIT. In addition, vaccine-induced immune thrombotic thrombocytopenia (VITT) is now a part of immunothrombotic disorders, and guidelines have been created concerning an anticoagulant strategy in this case. This literature review aims to summarize available data on HIT, APS, and VITT treatments and define the use of DOACs in therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".