Risk of bleeding in patients with essential thrombocythemia and extreme thrombocytosis
Bibliographic record
Abstract
ABSTRACT: Approximately 25% of patients with essential thrombocythemia (ET) present with extreme thrombocytosis (ExT), defined as having a platelet count ≥1000 × 109/L. ExT patients may have an increased bleeding risk associated with acquired von Willebrand syndrome. We retrospectively analyzed the risk of bleeding and thrombosis in ExT vs non-ExT patients with ET at Dana-Farber Cancer Institute and Massachusetts General Hospital from 2014 to 2022 to inform treatment decisions. We abstracted the first major bleed, clinically relevant nonmajor bleed (CRNMB), and thrombotic events from medical records. We identified 128 ExT patients (28%) and 323 non-ExT patients (72%). Cumulative incidence of bleeding was not different in ExT vs non-ExT patients (21% vs 13% [P = .28] for major bleed; 16% vs 15% [P = .50] for CRNMB). Very low and low thrombotic risk ExT patients were more likely to be cytoreduced than very low- and low-risk non-ExT patients (69% vs 50% [P = .060] for very low risk; 83% vs 53% [P = .0059] for low risk). However, we found no differences in bleeding between ExT and non-ExT patients when restricting the risk of bleed from diagnosis to cytoreduction start date (28% vs 19% [P = .29] for major bleed; 24% vs 22% [P = .75] for CRNMB). Cumulative incidence of thrombosis was also not different between ExT and non-ExT patients (28% vs 25%; P = .98). This suggests that cytoreduction may not be necessary to reduce bleeding risk based only on a platelet count of 1 million. We identified novel risk factors for bleeding in patients with ET including diabetes mellitus and the DNMT3A mutation.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".