Benchmarking nazionale della sopravvivenza per stadio alla diagnosi dei tumori infantili in Italia (BENCHISTA-ITA): protocollo di studio.
Bibliographic record
Abstract
BACKGROUND: survival rates for childhood cancers have significantly improved over recent decades, with 5-year survival now approaching 90% for many types. However, documented variations in survival across European countries and Italian regions highlight the need to address inequalities. One of the most critical prognostic factors is the extent of tumour spread at diagnosis (tumour stage). OBJECTIVES: the BENCHISTA-ITA aims to enhance understanding of regional differences in childhood cancer survival and to promote the widespread adoption of the Toronto Guidelines (TG) by Italian cancer registries for the most common solid paediatric tumours. DESIGN: the study will examine stage distribution and survival for nine solid paediatric cancers: medulloblastoma, neuroblastoma, Wilms tumour, retinoblastoma, and ependymoma (age: 0-14 years), as well as astrocytoma, osteosarcoma, Ewing sarcoma, and rhabdomyosarcoma (age: 0-19 years). SETTING AND PARTICIPANTS: the study will include all children under 15 or 20 years (depending on the tumour type) diagnosed between 01.01.2013 and 31.12.2017, with relevant histological codes. Participating Italian cancer registries will assign tumour stage at diagnosis using the Toronto Guidelines. STATISTICAL ANALYSIS: the statistical power to detect differences in stage distribution and survival rates among regions is limited by the number of incident cases per tumour type and region. Therefore, analyses will be descriptive, with 95% confidence intervals. Overall survival for each tumour type will be estimated using the Kaplan-Meier method. CONCLUSIONS: BENCHISTA-ITA represents an important step toward a more complete and standardized registration of childhood cancers in Italy. The results may support targeted interventions to reduce inequalities and improve outcomes for paediatric patients.
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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 | 0.078 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".