Validation of a predictive model for identifying an increased risk for thromboembolism in children with acute lymphoblastic leukemia: results of a multicenter cohort study
Bibliographic record
Abstract
Among risk factors for developing thromboembolism (VTE) in children with acute lymphoblastic leukemia were Escherichia coli asparaginase, concomitant steroid use, presence of central venous lines, and thrombophilic abnormalities. Developing a predictive model for determining children at increased risk would be beneficial in targeting interventional studies to high-risk groups (HRGs). Predictive variables were incorporated into a risk assessment model, which was evaluated in 456 children and then validated in 339 patients. VTE risk by score was no greater than 2.5 for low-risk group (LRG) and greater than 2.5 for HRG. VTE rates at 3.5 months (validation cohorts) were 2.5% in LRG and 64.7% in HRG. In multivariate analysis adjusted for age, duration of asparaginase administration, enoxaparin prophylaxis, and T-immunophenotype, the HRG was significantly associated with VTE compared with the LRG (hazard/95% confidence interval [CI], 8.22/1.85-36.53). Model specificity was 96.2% and sensitivity was 63.2%. As secondary objective we investigated the use of enoxaparin for VTE prophylaxis in the HRG. HRG patients without enoxaparin prophylaxis showed a significantly reduced thrombosis-free survival compared with children on low-molecular-weight heparin (LMWH). On the basis of the high specificity, the model may identify children with leukemia at risk of VTE. LMWH may help prevent VTE in the HRG; this warrants assessment in larger cooperative clinical trials.
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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 | 0.001 | 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".