Thrombosis is associated with worse survival in children with acute lymphoblastic leukemia: A report from <scp>CYP‐C</scp>
Bibliographic record
Abstract
There are conflicting data about whether the development of cancer-associated thrombo-embolism (TE) negatively impacts survival in children. The objective was to determine whether TE during treatment was associated with overall survival (OS) and event-free survival (EFS) in children with acute lymphoblastic leukemia (ALL). We performed a population-based retrospective cohort study using the Cancer in Young People-Canada registry. Children <15 years of age were diagnosed with de novo ALL (2000-2016). The primary exposure variable was radiologically-confirmed thrombo-embolism requiring medical intervention. Multivariable Cox regression models were used to determine the impact of thrombo-embolism on survival, where TE was time-dependent. We included 2006 children (median age: 4 years, 88.5% precursor B-cell ALL). Thrombo-embolism occurred in 113 patients (5.6%), at a median time of 107 days (interquartile range: 35-184 days) after ALL diagnosis. Among standard/low-risk patients, 41/1165 (3.5%) developed TE while among high/very high-risk patients, 72/841 (8.6%) developed TE. Patients with TE had a significantly worse OS (adjusted HR [aHR] of death: 2.61, 95% CI: 1.62-4.22, p < 0.001) and EFS (aHR of an event [death, relapse, second malignancy]: 2.03, 95% CI: 1.35-3.05, p = 0.001), compared with patients without TE. No statistically significant difference was seen in standard/low risk ALL for OS and EFS, but TE was associated with a significantly lower OS and EFS in children with high/very high-risk ALL (aHR of death: 2.90, 95% CI: 1.79-4.72, p < 0.001; aHR of an event: 2.02, 95% CI: 1.30-3.12, p = 0.002). Thus, TE led to a statistically significant reduction in OS and EFS in children with high risk/very high-risk leukemia.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".