Childhood cancer registries in Ontario, Canada: Lessons learned from a comparison of two registries
Bibliographic record
Abstract
Two childhood cancer registries exist in Ontario. One (POGO) accrues by active registration by pediatric cancer centers, utilizing a histologically based classification system. The other accrues by passive linkage within a larger adult oriented cancer registry (OCR) using a topographically based classification. A reportedly high incidence of childhood cancer based on the latter registry prompted a comparison of the content of the two registries over a 2-year period with the hypothesis that there would be systematic accrual errors. All registrations in both registries for the specified period were reviewed systematically and validated by pathology reports. A small number (2.6%) of registrations in the passive registry were not incident cases, while 2 particular pathologic diagnoses were included in the histologically based registry and not the topographically based registry. These were low grade gliomas and Langerhans cell histiocytosis (LCH). The validated annual incident rate (15.6 per 100000 children 0-14 years of age, excluding LCH) is slightly higher than that reported in other industrialized countries. Ninety-six percent of children aged 0-14 were treated in pediatric oncology centers, while only 46% of adolescents aged 15-17 were treated in such centers. Of the remaining adolescents, more than one-third had lymphomas. This maldistribution of care provided for the young adolescent population may compromise their survival prospects. The results of this study should prompt revision of health care policy and patterns of service delivery.
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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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".