Thromboembolism Incidence and Risk Factors in Children with Cancer: A Population-Based Cohort Study
Bibliographic record
Abstract
Abstract There is conflicting information about the epidemiology of thromboembolism (TE) in paediatric oncology. Objectives were to describe the incidence and risk factors of TE in children with cancer. We included all children with cancer less than 15 years of age diagnosed from 2001 to 2016, treated at one of the 12 Canadian paediatric centres outside of Ontario and entered into the Cancer in Young People-Canada database. Potential risk factors for TE were evaluated using Cox proportional hazards regression stratified by haematological malignancies versus solid tumours. Factors associated with vascular access- and non-vascular access-related TE were compared using chi-square or Fisher's exact tests. Of the 7,471 children included, 283 experienced TE requiring medical intervention; cumulative incidence of TE at 5 years was 3.8 ± 0.2% and 0.36% ± 0.07% for life-threatening or fatal TE. For haematological malignancies, the following factors were associated with TE in multivariable regression: age < 1 year, 5 to 9.99 years and 10 to 14.99 years (relative to age 1–4.99 years), haematopoietic stem cell transplant (hazard ratio [HR] = 1.49, 95% confidence interval [CI], 1.00–2.32), anthracyclines (HR = 2.21, 95% CI, 1.12–4.37) and asparaginase (HR = 1.68, 95% CI, 1.15–2.44). For solid tumours, obesity (HR = 1.92, 95% CI, 1.01–3.68), surgery (HR = 2.70, 95% CI, 1.44–5.08), radiation (HR = 47.51, 95% CI, 24.01–94.01), anthracyclines (HR = 2.74, 95% CI, 1.29–5.82) and platinum agents (HR = 2.26, 95% CI, 1.19–4.28) were associated with TE. Life-threatening and fatal TEs were more common among non-vascular access TEs (14.5% vs. 3.3% p = 0.001). In a population-based cohort, 4% of children with cancer developed a clinically significant TE. Accurate risk stratification tools are needed specific to malignancy type.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".