Incidence and survival for childhood cancer by endorsed non‐stage prognostic indicators in Australia
Bibliographic record
Abstract
BACKGROUND: An international expert panel recently recommended 15 'non-stage prognostic indicators' (NSPIs) across eight childhood cancers, classified as essential or additional, for collection in population-based cancer registries. We aimed to describe the incidence distribution and survival of each of these NSPIs. PROCEDURES: Cases were extracted from the Australian Childhood Cancer Registry. The study cohort (n = 4187) comprised all children aged under 15 years diagnosed with an eligible cancer between 2010 and 2018, with follow-up until 31 December 2020. NSPI data were collected directly from each patient's medical records. Differences in 5-year relative survival were assessed using multivariable flexible parametric models, adjusted for sex and age group at diagnosis. RESULTS: The availability of data varied, exceeding 85% for all essential NSPIs apart from histologic subtype for Wilms tumours (69%) and lineage for acute lymphoblastic leukaemia (78%). Information on additional NSPIs tended to be recorded less often, particularly cytogenetic subtype for non-alveolar rhabdomyosarcoma (28%) and astrocytoma (4%). Eight NSPIs exhibited a significant difference in survival, with the largest disparity occurring among children with astrocytoma according to tumour grade (5-year relative survival of 18% for grade IV disease compared with 99% for grade I disease; p < .001). CONCLUSIONS: Our findings demonstrate that most of the recommended NSPIs can be retrieved from medical records in Australia in recent years, allowing the capability of assessing survival within patient subgroups of clinical interest. Reporting of NSPI data has the capability to inform local and global understanding of population-level disparities in childhood cancer survival.
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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 | 0.002 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".