Long-term incidence of venous thromboembolism (VTE) among survivors of childhood cancer: A report from the Childhood Cancer Survivor Study (CCSS).
Bibliographic record
Abstract
10562 Background: This study aimed to estimate the incidence of late-occurring VTE among survivors of childhood cancer, and to identify associated demographic and clinical factors that define high-risk subgroups for potential screening and prevention. Methods: Using data from CCSS, a multi-institutional, longitudinal cohort of 5-year survivors of childhood cancer (diagnosed 1970-1999) and their siblings, the primary endpoint of self-reported late VTE (occurring ≥5 years after diagnosis) was estimated using multivariable piecewise exponential models adjusted for age, sex, and race. Generalized estimating equations accounted for potential within-family correlation where applicable. Results: Among 23,601 survivors and 5051 siblings, the incidence of VTE was 1.15 and 0.48 events per 1000 person-years, respectively. For survivors, median age at last follow-up was 28.6 years (range 5.6-58.3) and median follow-up time from diagnosis was 21.2 years (range 5.0-39.3). The adjusted rate ratio (RR) for survivors compared to siblings was 2.2 (95% confidence interval [CI] = 1.7-2.8, P< 0.01). Among survivors, risk factors for VTE included BMI≥30kg/m2 (ref. BMI 18.5-24.5; RR = 1.5, CI = 1.2-2.0, P< 0.01), increasing number of severe or life-threatening (i.e. CTCAE grades 3 or 4) non-VTE chronic conditions (ref. 0 conditions; 1-2 conditions: RR = 2.5, CI = 2.0-3.1, P< 0.01 ; ≥3 conditions: RR = 3.5, CI = 2.5-4.9, P< 0.01), and cancer recurrence or second malignant neoplasm (RR = 3.5, CI = 2.7-4.6, P< 0.01). Incidence of late VTE was associated with increased subsequent mortality, independent of non-VTE chronic conditions (RR 2.2, 95% CI = 1.7-2.8, P< 0.01). Conclusions: Survivors of childhood cancer remain at increased risk for VTE across their lifespan. While typically not causal, late VTE was associated with subsequent mortality. Care providers should be aware of this increased risk and consider interventions that target modifiable co-morbidities such as obesity. Surveillance and education should be directed toward high-risk survivors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".