Cardiovascular and Hematologic Complications of COVID-19 Vaccines
Bibliographic record
Abstract
COVID-19 is a prothrombotic and cardiac-damaging disease. There are 4 vaccines against COVID-19 currently approved in North America, including the mRNA vaccines by Pfizer and Moderna, and the adenovirus vector vaccines by Johnson and Johnson and AstraZeneca. These vaccines have been proven effective in reducing morbidity and preventing mortality in patients who were exposed to COVID-19 infection, but the vaccines have also been associated with complications. Vaccine-induced thrombotic thrombocytopenia (VITT) has a similar pathogenesis to heparin-induced thrombocytopenia, with an inappropriate immune response leading to platelet activation, consumption of platelets, and thrombosis. It appears to be more common with the adenovirus vector vaccines. Secondary immune thrombocytopenic purpura has been reported with all COVID-19 vaccines and is distinct from VITT because there is no sign of platelet activation or thrombotic events. Myocarditis and pericarditis are often reported in young males following mRNA vaccines and is often associated with a full recovery. The long-term effects of VITT, secondary immune thrombocytopenic purpura, myocarditis, and pericarditis secondary to COVID-19 vaccines have yet to be elucidated. Continued surveillance for these complications after vaccination is crucial for accurate diagnosis and effective management. Patients should consult their physicians regarding repeated vaccine doses after experiencing an adverse effect.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".