Risk factors and clinical impact of thrombosis during induction chemotherapy for pediatric acute lymphoblastic leukemia: A report from <scp>CYP‐C</scp>
Bibliographic record
Abstract
Abstract Thromboembolism (TE) is associated with reduced survival in pediatric acute lymphoblastic leukemia (ALL). It has been hypothesized that TE might signal leukemic aggressiveness. The objective was to determine risk factors for TE during ALL induction (TEind) therapy and whether TEind is associated with treatment refractoriness. This retrospective cohort study using the population‐based Cancer in Young People Canada (CYP‐C) registry included children <15 years of age diagnosed with ALL (2000–2019) and treated at one of 12 Canadian pediatric centers outside of Ontario. Univariate and multivariable logistic regression models were used to determine risk factors for TEind and whether TEind predicted induction failure and ALL treatment intensification. The impact of TEind on overall and event‐free survival was estimated using Cox proportional hazard regression models. The study included 2589 children, of which 45 (1.7%) developed a TEind. Age (<1 year and ≥10 years vs. 1–<10 years), T‐cell phenotype, high‐risk ALL, and central nervous system involvement were all associated with TEind in univariate analysis. Age and T‐cell phenotype remained independent predictors of TEind in multivariable analysis. Induction failure occurred in 53 patients (2.1%). TEind was not associated with induction failure (OR: not estimable) or treatment intensification (adjusted OR [95% CI]: 0.66 [0.26–1.69]). TEind was independently associated with overall survival (adjusted HR [95% CI]: 2.54 [1.20–5.03]) but not event‐free survival (adjusted HR [95% CI] 1.86 [0.98–3.51]). In this population‐based study of children treated with contemporary chemotherapy protocols, TEind was associated with age and T‐cell phenotype and mortality but did not predict induction failure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".