Risk factors for arterial versus venous thrombosis in polycythemia vera: a single center experience in 587 patients
Bibliographic record
Abstract
Abstract In a recent International Working Group on Myeloproliferative Neoplasms Research and Treatment (IWG-MRT) study, prior arterial events and hypertension were predictors of subsequent arterial thrombosis whereas prior venous events and age ≥65 years predicted venous thrombosis in polycythemia vera (PV). In the current study, we sought to validate the above findings and identify additional predictors of arterial versus venous thrombosis. At a median follow up of 109 months, thrombosis after diagnosis occurred in 128 (22%) patients; 82 (14%) arterial and 57 (10%) venous events. On multivariate analysis, prior arterial events (<0.0001), hyperlipidemia ( p = 0.03), and hypertension ( p = 0.02) predicted subsequent arterial events. In comparison, prior venous events ( p = 0.05), leukocytosis ≥11 × 10 9 /L ( p = 0.002), and major hemorrhage ( p = 0.02) were predictors of subsequent venous events. Salient associations with arterial thrombosis included age ≥ 60 years, hypertension, diabetes, hyperlipidemia and normal karyotype whereas age ≤ 60 years, females, palpable splenomegaly and history of major hemorrhage were associated with venous thrombosis. TET2 or ASXL1 mutations did not impact arterial nor venous thrombosis. In conclusion, we identify distinct associations for arterial versus venous thrombosis in PV and confirm that a prior arterial or venous thrombotic event is the most reliable predictor of subsequent events.
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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 | 0.001 | 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) | 0.002 | 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".