Improving Population-Wide Collection of Stage at Diagnosis for Childhood Cancer: International Collaboration and Progress
Bibliographic record
Abstract
Background: There are huge international disparities in childhood cancer survival. The International Agency for Research on Cancer's Global Initiative for Cancer Registry Development has improved cancer registry coverage of the world's population, particularly in low- and middle-income countries (LMICs). However, for virtually all registries around the world, the challenge remains of collecting comparable and population-wide information on stage at diagnosis. This information is essential to understand and address disparities in outcomes. In response to this, a UICC-endorsed set of consensus guidelines for assigning stage for 16 of the most common types of childhood cancer was recently developed (the Toronto Guidelines), for use by population registries in both high and LMICs. Aim: To trial the Toronto Guidelines on a population-basis, and develop a structured protocol, suitable for global implementation, for collecting the required data elements and assigning cancer stage at diagnosis for childhood cancer. Methods: Using an innovative approach, data items as defined in the Toronto Guidelines were gathered from the medical record and entered electronically. Stage at diagnosis was assigned automatically using computer algorithms, thus reducing errors and maximizing consistency. Data collection and assignment of stage were incorporated into an online platform that was then trialed in the national childhood cancer registries of Australia and New Zealand for cases diagnosed between 2006 and 2014. Results: Stage at diagnosis was successfully assigned for 94% of all eligible patients (n=1662) across both countries. In contrast, stage as recorded by the treating clinician was located in the medical record for only 39% of cases in Australia. Conclusion: Practical implementation of the Toronto Guidelines has been highly successful to date and further testing is planned in LMICs. This approach has the potential to improve global epidemiologic monitoring of childhood cancer and lead to better understanding of the reasons underlying disparities in outcome.
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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 | 0.420 | 0.373 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".