Improving the Connection Between Population-Based and National Clinical Pediatric Cancer Registries—A Pilot Study on Neuroblastoma: The Italian BENCHISTA-Ita Project
Bibliographic record
Abstract
Background: Childhood cancers (CC) are rare and treatable; however, survival rates vary across Europe, with poorer outcomes in Eastern countries. Stage at diagnosis is one of the major prognostic factors. The BENCHISTA-Ita project aims to promote the use of the Toronto Guidelines by population-based cancer registries (PBCR) to standardize stage at diagnosis data collection. In Italy, regional cancer registries ensure data quality, while national clinical registries centralize pediatric cancer data. The aim of the study is to promote the linkage between PBCRs and national clinical registries to enhance data completeness. This study presents the results of linking BENCHISTA-Ita neuroblastoma cases with the Italian Neuroblastoma Registry (RINB) as a pilot study. Methods: The linkage process involved probabilistic matching using R software, considering variables such as sex, year of birth, and residence at diagnosis. The study included 294 neuroblastoma cases from BENCHISTA-Ita and 578 from RINB, diagnosed between 2013 and 2017. Results: Results showed that 272 of 294 BENCHISTA-Ita cases matched with RINB cases, improving the completeness of clinical variables such as stage at diagnosis, N-Myc amplification, and relapse/progression. The linkage increased the completeness of clinical variables in PBCR, such as stage at diagnosis from 81% to 99%, N-Myc from 47% to 86%, and relapse/progression from 68% to 98%. Additionally, the linkage improved RINB’s follow-up completeness from 59% to 97% and added new cases in both PBCR and RINB. Conclusions: This linkage demonstrates the potential to enrich both databases, improving data quality and harmonization of cancer indicators. The study highlights the importance of collaboration between PBCRs and clinical registries to ensure comprehensive data collection and enhance more informative population-based studies. Future efforts will focus on expanding the linkage to other national clinical registries.
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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 | 0.046 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".