Epidemiology and clinical risk factors predisposing to thromboembolism in children with cancer
Bibliographic record
Abstract
PURPOSE: The prevalence and risk factors for thromboembolism (TE) in children with cancer are largely unknown. This retrospective cohort study aims to determine the epidemiology of TE and to identify potential risk factors for TE in children with cancer. METHODS: We used logistic regression to determine the association of age (<10 years vs. > or =10 years), gender, type of cancer, presence or absence of intra-thoracic disease (mediastinal mass or any primary or metastatic pulmonary disease), type of central venous line (CVL) and CVL-dysfunction (difficulty of blood draw, infusion or documented CVL infection) on the risk of developing TE. RESULTS: Fifty-seven of 726 patients [7.9%; 95% confidence intervals (CI); 6.0,10.0] developed TE; children with brain tumors (n = 201) had significantly lower prevalence of TE (0.5%; P < 0.001). Older patients had increased risk of developing TE compared to younger patients [Odds ratios (OR) 1.8; 95% CI; 1.0,3.2; P = 0.036]. Children with acute lymphoblastic leukemia (ALL) (OR 4.6; 95% CI; 1.8, 12.3; P = 0.002), lymphoma (OR 3.8; 95% CI; 1.3, 11.1; P = 0.016), and sarcoma (OR 4.3; 95% CI; 1.4, 13.3; P = 0.012) had an increased risk of TE. Subgroup analyses showed that patients with CVL-dysfunction and intra-thoracic disease had a higher prevalence of TE compared to those without CVL-dysfunction (22.8% vs. 8.8%; 95% CI; 4.0, 24.3; P = 0.006) and intra-thoracic disease (18.0% vs. 6.1%; 95% CI; 2.4, 21.4; P = 0.02). CONCLUSIONS: TE is common in children with cancer. Age and type of cancer are independent risk factors for TE in children with non-CNS cancers. CVL-dysfunction and intra-thoracic disease are significantly associated with the diagnosis of TE.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".