Thromboembolic Complications in Pediatric Hematologic Malignancies
Bibliographic record
Abstract
Thromboembolism (TE) is an uncommon entity in childhood. Overall 25% of children with thrombosis and more than 40% of children with central venous line (CVL) -related TE enrolled on the Canadian Pediatric Thrombophilia Registry had underlying diagnosis of cancer. However, so far there are very little data describing the epidemiology of TE in children with cancer. Most of the available information in this area originates mainly from retrospective and some prospective observational cohort studies conducted in children with acute lymphoblastic leukemia (ALL). Although ALL has been the most common cancer reported in association with thrombosis in children, available data from small studies indicate that TE is equally common in children with acute myeloid leukemia and lymphoma. TE in association with leukemia and lymphoma seems to be a multifactorial entity. Potential risk factors include increased thrombin generation related to leukemia, age of the patients, use of CVL, chemotherapy including asparaginase and corticosteroids, infections, and inherited prothrombotic state. Management of TE in a child with cancer presents a unique challenge in terms of balancing risk versus benefit. Conservative therapy could lead to clot extension and risks of additional morbidity or mortality; however, chemotherapy-related thrombocytopenia and coagulopathy increase the risk of bleeding complications. In summary, TE is a frequent and serious complication in children with hematologic malignancies. More prospective studies are required to define the epidemiology, pathogenesis, and management of TE in children with hematologic malignancies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".