Outcomes of Cerebral Venous Thrombosis due to Vaccine-Induced Immune Thrombotic Thrombocytopenia After the Acute Phase
Bibliographic record
Abstract
BACKGROUND: Cerebral venous thrombosis (CVT) due to vaccine-induced immune thrombotic thrombocytopenia (VITT) is a severe condition, with high in-hospital mortality rates. Here, we report clinical outcomes of patients with CVT-VITT after SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) vaccination who survived initial hospitalization. METHODS: We used data from an international registry of patients who developed CVT within 28 days of SARS-CoV-2 vaccination, collected until February 10, 2022. VITT diagnosis was classified based on the Pavord criteria. Outcomes were mortality, functional independence (modified Rankin Scale score 0-2), VITT relapse, new thrombosis, and bleeding events (all after discharge from initial hospitalization). RESULTS: Of 107 CVT-VITT cases, 43 (40%) died during initial hospitalization. Of the remaining 64 patients, follow-up data were available for 60 (94%) patients (37 definite VITT, 9 probable VITT, and 14 possible VITT). Median age was 40 years and 45/60 (75%) patients were women. Median follow-up time was 150 days (interquartile range, 94-194). Two patients died during follow-up (3% [95% CI, 1%-11%). Functional independence was achieved by 53/60 (88% [95% CI, 78%-94%]) patients. No new venous or arterial thrombotic events were reported. One patient developed a major bleeding during follow-up (fatal intracerebral bleed). CONCLUSIONS: In contrast to the high mortality of CVT-VITT in the acute phase, mortality among patients who survived the initial hospitalization was low, new thrombotic events did not occur, and bleeding events were rare. Approximately 9 out of 10 CVT-VITT patients who survived the acute phase were functionally independent at follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".