Predictors of thrombosis in children receiving therapy for acute lymphoblastic leukemia: Results from Dana‐Farber Cancer Institute ALL Consortium trial 05‐001
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Although thromboembolism (TE) is a serious complication in patients with acute lymphoblastic leukemia (ALL), thromboprophylaxis is not commonly used due to the inherent bleeding risk in this population. Identifying prothrombotic risk factors will help target thromboprophylaxis to those at highest thrombotic risk. We aimed to define predictors and the impact of TE on ALL outcome in children (1-18 years) treated on the Dana-Farber Cancer Institute ALL 05-001 trial. METHODS: Clinical and laboratory data including TE events were prospectively collected. PCR-based allelic discrimination assay identified single-nucleotide polymorphisms (SNP) for prothrombin G20210A (rs1799963) and Factor V G1691A (rs6025). Univariate and multivariable competing risk regression models evaluated the effect of diagnostic clinical (age, sex, body mass index, ALL-immunophenotype, risk group) and laboratory variables (presenting leukocyte count, blood group, SNPs) on the cumulative incidence of TE. Cox regression modeling explored the impact of TE on survival. RESULTS: Of 794 patients [median age 4.97 (range, 1.04-17.96) years; males 441], 100 developed TE; 25-month cumulative incidence 13.0% (95% CI, 10.7%-15.5%). Univariate analyses identified older age (≥10 years), presenting leucocyte count, T-ALL, high-risk ALL, and non-O blood group as risk factors. Age and non-O blood group were independent predictors of TE on multivariable regression; the blood group impact being most evident in patients 1-5 years of age (P = 0.011). TE did not impact survival. Induction TE was independently associated with induction failure (OR 6.45; 95% CI, 1.64-25.47; P = 0.008). CONCLUSION: We recommend further evaluation of these risk factors and consideration of thromboprophylaxis for patients ≥10 years (especially those ≥15 years) when receiving asparaginase.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".