Population-Based Survival of Childhood and Adolescent Cancers (0–19 Years) in Madrid: Analysis by Sex, Age, Tumour Type, and Stage
Bibliographic record
Abstract
Background/Objectives: In Europe, over 80% of children diagnosed with cancer survive at least 5 years. To improve cancer monitoring, the Paediatric Population-Based Cancer Registry (PCRM) was established in the Community of Madrid. This study aimed to describe population-based 1-, 3- and 5-year survival for children and adolescents diagnosed with cancer, by sex, age, tumour type and stage at diagnosis. Methods: Data were extracted from the PCRM, which reviews all cases identified through integrated primary care, hospital discharge, and mortality data, using electronic medical records. Patients aged 0–19 diagnosed with primary malignant cancer between 2015 and 2018 were included, with follow-up for vital status through October 2024. Stage was classified using the 2014 Toronto Childhood Cancer Staging Guidelines (tier 2). Kaplan–Meier methods were used to estimate survival, and log-rank tests assessed group differences. Cox regression was used to quantify the effect of localized vs. advanced disease. Results: The analysis included 862 patients. Most frequent cancers were leukaemia (24.1%), lymphomas (22.2%) and central nervous system (CNS) tumours (12.6%). Stage was assigned to 88.4% tumours. Overall survival was 93.6% in 1 year and 85.9% in 5 years. Five-year survival was 83.7% for leukaemia, 97.4% for lymphomas, 66.1% for CNS tumours; 85.8% in boys vs. 85.9% in girls (p = 0.908); 85.2% in children aged 0–14 years vs. 87.8% in adolescents aged 15–19 years (p = 0.314); and 69.9% for advanced vs. 89.7% for early-stage (p < 0.001), with a 3.3-fold higher mortality risk. Conclusions: This population-based study offers promising survival estimates reaching 86% globally at 5 years while revealing differences by cancer type and stage. It also highlights the Toronto Guidelines as a valuable tool for standardizing cancer registry methods and providing useful epidemiological indicators.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".