Thrombotic Thrombocytopenia after ChAdOx1 nCoV-19 Vaccination
Bibliographic record
Abstract
Thrombotic Thrombocytopenia after ChAdOx1 nCoV-19 VaccinationTo the Editor: Greinacher et al. (April 9) 1 report on the development of platelet-activating antibodies against platelet factor 4 (PF4) after ChAdOx1 nCoV-19 vaccination in patients without previous exposure to heparin.These patients had clinical features that mimicked autoimmune heparininduced thrombocytopenia.In serum samples obtained from these patients, platelets were shown to be activated in the absence of heparin.Platelet activation was shown to be inhibited when high doses of heparin were added to the serum samples.Thrombocytopenia after the administration of adenoviral gene transfer vectors has been reported.Platelet activation with the formation of adenovirus-platelet-leukocyte complexes leading to accelerated platelet clearance in the liver and thrombocytopenia is common after adenovirus administration.P-selectin and von Willebrand factor are critically involved in a complex interplay between platelets, leukocytes, and endothelium in mediating accelerated platelet clearance. 2,3hese adenovirus-platelet-leukocyte complexes are then taken up by the liver through interaction with membrane-associated heparan sulfate proteoglycans, which act as receptors for viral entry. 4 Heparin can lead to dose-dependent inhibition of this interaction. 5Perhaps the interaction with heparan sulfate may sensitize the body to the development of platelet-activating antibodies against PF4 and lead to heparin-induced thrombocytopenia and thrombosis.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.005 |
| 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".