Stage at diagnosis for children with blood cancers in Australia: Application of the Toronto Paediatric Cancer Stage Guidelines in a population‐based national childhood cancer registry
Bibliographic record
Abstract
Abstract Background Information on stage at diagnosis for childhood blood cancers is essential for surveillance but is not available on a population basis in most countries. Our aim was to apply the internationally endorsed Toronto Paediatric Cancer Stage Guidelines to children (<15 years) with acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), Hodgkin lymphoma (HL), or non‐Hodgkin lymphoma (NHL) and to assess differences in survival by stage at diagnosis. Procedure Stage was defined by extent of involvement of the central nervous system (CNS) for ALL and AML and using the Ann Arbor and St Jude‐Murphy systems for HL and NHL, respectively. The study cohort was drawn from the population‐based Australian Childhood Cancer Registry, consisting of children diagnosed with one of these four blood cancers between 2006 and 2014 with follow‐up to 2015. Five‐year observed survival was estimated from the Kaplan–Meier method. Results Stage was assigned to 2201 of 2351 eligible patients (94%), ranging from 85% for AML to 95% for ALL, HL, and NHL. Survival following ALL varied from 94% (95% CI = 93%–95%) for CNS1 disease to 89% (95% CI = 79%–94%) for CNS2 (P = 0.07), whereas for AML there was essentially no difference in survival between CNS− (77%) and CNS+ disease (78%; P = 0.94). Nearly all children with HL survived for five years. There was a trend (P = 0.04) toward worsening survival with higher stage for NHL. Conclusions These results provide the first population‐wide picture of the distribution and outcomes for childhood blood cancers in Australia by extent of disease at diagnosis and provide a baseline for future comparisons.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".