Supportive Care in Pediatric Cancer: The Road to Prevention of Thrombosis
Bibliographic record
Abstract
The survival rate of children with cancer has increased impressively to almost 80% over the last decades as a result of improved diagnostic procedures and multimodal treatment strategies. Therefore, it becomes more and more important to prevent mortality and morbidity of treatment-associated complications, including venous thromboembolism (VTE). VTE occurs predominantly in children with acute lymphoblastic leukemia, lymphoma, and sarcoma. Pathogenesis of thrombosis in children with cancer is multifactorial. Thrombosis develops due to a combination of the primary disease itself, chemotherapy and supportive care, associated complications, and inherited prothrombotic risk factors probably contributed to the development of thrombosis in these children. Mortality as a result of VTE is low, but both symptomatic and asymptomatic thrombosis cause significant morbidity to justify primary thromboprophylaxis in children with cancer. Identification of risk factors is important to develop predictive models to identify patients at highest risk of thrombosis. Due to variations in risk factors, these models should be tailored to treatment protocols and patient populations. Multicenter studies are needed to investigate which prophylactic strategies are effective and safe to prevent thrombosis in children with cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".