Pediatric hepatic cancer incidence and survival: 30‐year trends in Ontario, Canada; the United States; and Australia
Bibliographic record
Abstract
BACKGROUND: Pediatric hepatic cancer is a rare malignancy, comprising only approximately 2% of all cancers diagnosed in children aged <15 years. The authors sought to describe trends in pediatric hepatic cancer incidence and survival in Ontario, Canada; the United States; and Australia. METHODS: Children aged <15 years who were diagnosed with hepatic cancer from 1985 through 2013 were ascertained through population-based registries and followed from the time of diagnosis until December 31, 2015. Age-standardized incidence and 5-year relative survival were calculated for each jurisdiction. Multivariable flexible parametric survival models were used to explore predictors of hepatic cancer mortality. RESULTS: A total of 794 children were identified in Ontario (148 children), the United States (400 children), and Australia (246 children). The average annual incidence increased by 2.2% (95% CI, 0.5%-4.0%) in Australia, 2.1% (95% CI, 0.9%-3.3%) in the United States, and 1.3% (95% CI, -0.4% to 3.0%) in Ontario. The 5-year relative survival rate improved from 60% to 82% (P = .08) in Ontario and 62% to 78% (P = .02) in the United States between the diagnostic periods 1985 through 1994 and 2005 through 2013, whereas in Australia the rate remained constant (between 74% and 77%) during the study period. On multivariable analysis, there was no significant difference noted with regard to the hazard of death between jurisdictions (P = .06). Older age, the presence of metastatic disease, and being diagnosed with hepatocellular carcinoma were found to be associated with mortality. CONCLUSIONS: The incidence of hepatic cancer in children appears to have increased over the last 30 years in Australia and North America. Survival differences between Australia; Ontario, Canada; and the United States observed in the 1980s and 1990s were no longer apparent and only marginal geographical differences in the hazard of mortality were observed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".