Vaccine-induced immune thrombotic thrombocytopenia: Updates in pathobiology and diagnosis
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a viral respiratory infection caused by the severe acute respiratory syndrome virus (SARS-CoV-2). Vaccines that protect against SARS-CoV-2 infection have been widely employed to reduce the incidence of symptomatic and severe disease. However, adenovirus-based SARS-CoV-2 vaccines can cause a rare, thrombotic disorder termed vaccine-induced immune thrombotic thrombocytopenia (VITT). VITT often develops in the first 5 to 30 days following vaccination and is characterized by thrombocytopenia and thrombosis in unusual locations (e.g., cerebral venous sinus thrombosis). The diagnosis is confirmed by testing for anti-PF4 antibodies, as these antibodies are capable of platelet activation without any cofactor. It can be clinically challenging to differentiate VITT from a similar disorder called heparin-induced thrombocytopenia (HIT), since heparin is commonly used in hospitalized patients. VITT and HIT have similar pathobiology and clinical manifestations but important differences in testing including the need for PF4-enhanced functional assays and the poor reliability of rapid immunoassays for the detection of anti-platelet factor 4 (PF4) antibodies. In this review we summarize the epidemiology of VITT; highlight similarities and differences between HIT and VITT; and provide an update on the clinical diagnosis of VITT.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".