Treatment of vaccine-induced immune thrombotic thrombocytopenia (VITT)
Bibliographic record
Abstract
Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a novel prothrombotic disorder characterized by thrombosis, thrombocytopenia, and disseminated intravascular coagulation identified in hundreds of recipients of ChAdOx1 nCoV-19 (Oxford/AstraZeneca), an adenovirus vector coronavirus disease 2019 (COVID-19) vaccine. VITT resembles heparin-induced thrombocytopenia (HIT) in that patients have platelet-activating anti-platelet factor 4 antibodies; however, whereas heparin typically enhances platelet activation by HIT antibodies, VITT antibody-induced platelet activation is often inhibited in vitro by pharmacological concentrations of heparin. Further, the thrombotic complications in VITT feature much higher frequencies of atypical thrombosis, most notably cerebral vein thrombosis and splanchnic vein thrombosis, compared with HIT. In this review, we outline the treatments that have been used to manage this novel condition since its recognition in March 2021, including anticoagulation, high-dose intravenous immune globulin, therapeutic plasma exchange, corticosteroids, rituximab, and eculizumab. We discuss the controversial issue of whether heparin, which often inhibits VITT antibody-induced platelet activation, is harmful in the treatment of VITT. We also describe a case of "long VITT," describing the treatment challenges resulting from platelet-activating anti-PF4 antibodies that persisted for more than 9 months.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".