Vaccine‐induced immune thrombotic thrombocytopenia presenting with normal platelet count
Bibliographic record
Abstract
Adenoviral-vector based vaccines for coronavirus disease 2019 (COVID-19) have been linked with a thrombotic syndrome, vaccine-induced thrombotic thrombocytopenia (VITT). A key clinical question is whether VITT can be reliably ruled out by the absence of thrombocytopenia. We report on three patients who presented to our institute with this syndrome. Noteworthy in our presentations are two patients who presented for medical assessment of thrombotic symptoms with a normal platelet count, one preceding and one following a period of thrombocytopenia. Prompt diagnosis of VITT is critical to prevent rapid patient decline. We provide herein a new diagnostic algorithm that we believe will help optimally capture case presentations of VITT. These cases broaden and refine our understanding of the disease process and highlight to practitioners that VITT cannot be adequately ruled out by thrombocytopenia alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".