Staging childhood cancers in Europe: Application of the Toronto stage principles for neuroblastoma and Wilms tumour. The JARC pilot study
Bibliographic record
Abstract
BACKGROUND: The 'Toronto consensus principles and guidelines' (TG) provided paediatric-specific staging system affordable by population-based cancer registries (CRs). Within the European Rare Cancers Joint Action, a pilot study of the application of TG for childhood cancer (CC) was conducted to test the ability of CRs to reconstruct stage, describe stage across countries and assess survival by stage. PROCEDURE: Twenty-five CRs representing 15 countries contributed data on a representative sample of patients with neuroblastoma (NB) and Wilms tumour (WT) <15 years, diagnosed between 2000 and 2016. Outcome was calculated by Kaplan-Meier method and by Cox regression model. RESULTS: Stage was reconstructed for 95% of cases. Around half of the children had localised or locoregional disease at diagnosis. The proportion of metastatic cases was 38% for NB and 13% for WT. Three-year survival was >90% for locoregional cases both of NB and WT, 58% for NB M-stage and 77% for WT stage-IV. Older age was associated with more advanced stage. CONCLUSIONS: European CRs were able to reconstruct stage according to the TG. Stage should be included in the routine collection of variables. Stage information had clear prognostic value and should be used to investigate survival variations between countries or over time.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".