Thromboembolism in children with acute lymphoblastic leukaemia treated on Dana‐Farber Cancer Institute protocols: effect of age and risk stratification of disease
Bibliographic record
Abstract
Children with acute lymphoblastic leukaemia (ALL) are at increased risk for thromboembolism (TE). Identification of a susceptible population is crucial for effective thromboprophylaxis. However, the risk factors for ALL-associated TE are unclear. Concomitant asparaginase (ASP) and steroid therapy has been shown to increase the incidence of TE. Dana-Farber Cancer Institute (DFCI)-ALL protocols use a combination of ASP and steroids during the postinduction intensification phase when high-risk (HR) patients receive thrice the steroid-dose given to standard-risk (SR) patients. We studied prospectively assembled cohorts of children treated on two consecutive DFCI-ALL protocols to define the risk factors for symptomatic TE. Ten (11%) of 91 patients developed symptomatic TE; eight (seven HR) during intensification. Seven (44%) of 16 older patients (>/=10 years) compared with three of 75 (4%) younger patients developed TE (P < 0.0001). Nine of 35 (26%) HR and one of 56 (2%) SR patients developed TE (P = 0.0006). Gender, ALL-immunophenotype, steroid-type or ASP dosing schedule did not alter the risk but older age and HR-disease were factors predisposing to TE associated with DFCI-ALL protocols. Age-related risk may partly reflect the effect of ALL-risk stratification. Higher dose steroids combined with ASP may lead to an increased risk of TE in HR patients.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".