Thrombocytopenia with and without thrombosis following COVID-19 vaccination: long-term management
Bibliographic record
Abstract
Background: Since administration of COVID-19 vaccines, there has been growing evidence of thrombotic and thrombocytopenic events following vaccination. However, there remains limited data on long-term management of these adverse hematologic events. Key Clinical Question: We report on 9 patients presenting with thrombocytopenia following COVID-19 vaccination, with 4 subsequently diagnosed with vaccine-induced thrombocytopenia and thrombosis (VITT) and 5 with immune thrombocytopenia. Clinical Approach: A retrospective chart review was completed for adults >18 years of age presenting to a tertiary care center with new-onset thrombocytopenia occurring 4 to 42 days following COVID-19 vaccination. Presenting symptoms, laboratory investigations, and response to treatment are described. Conclusion: Two of 4 patients with VITT developed refractory thrombocytopenia successfully treated with intravenous immunoglobulin, corticosteroids, and plasma exchange therapy. Patients with VITT remained on anticoagulation for at least 9 months due to persistently positive diagnostic tests. Four of 5 patients with immune thrombocytopenia received intravenous immunoglobulin and corticosteroids with good recovery. Patients who received a subsequent COVID-19 mRNA vaccine had no adverse hematologic effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".