Development of a Clinical Prediction Rule for Venous Thromboembolism in Patients with Acute Leukemia
Bibliographic record
Abstract
Abstract Risk factors for venous thromboembolism in patients with solid tumors are well studied; however, studies in patients with acute leukemia are lacking. We conducted a retrospective cohort study of adult patients diagnosed with acute myeloid leukemia and acute lymphoblastic leukemia diagnosed between June 2006 and June 2017 at a tertiary care center in Canada. Potential predictors of venous thromboembolism were evaluated using logistic regression and a risk score was derived based on weighed variables and compared using survival analysis. Internal validation was conducted using nonparametric bootstrapping. A total of 501 leukemia patients (427 myeloid and 74 lymphoblastic) were included. Venous thromboembolism occurred in 77(15.3%) patients with 71 events occurring in the first year. A prediction score was derived and validated and it included: previous history of venous thromboembolism (3 points), lymphoblastic leukemia (2 points), and platelet count > 50 × 109/L at the time of diagnosis (1 point). The overall cumulative incidence of venous thromboembolism was 44% in the high-risk group (≥ 3 points) versus 10.5% in the low-risk group (0–2 points) and it was consistent at different follow-up periods (log-rank p < 0.001). We derived and internally validated a predictive score of venous thromboembolism risk in acute leukemia patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".