Cancer incidence, morbidity, and survival in Canadian first nation children: A Manitoba population‐based study from the cancer in young people in Canada (CYP‐C) registry
Bibliographic record
Abstract
BACKGROUND: Health disparities between Canadian First Nation (FN) people and the rest of the national population exist. No studies have specifically documented cancer-related health outcomes in Canadian FN children. The purpose of this study was to describe the incidence of pediatric malignancies in Manitoba FN children, and to compare morbidity patterns and survival between FN and non-FN children with cancer in the Canadian province of Manitoba. PROCEDURE: A retrospective, population-based review of all children (0-14.99 years) diagnosed with malignancy (2001-2008) in Manitoba, Canada was undertaken using the Cancer in Young People in Canada registry. FN children were compared to the non-FN population for markers of morbidity and survival. RESULTS: The average annual age-standardized incidence rate for all childhood cancers in FN children was 132 per 1,000,000 per year. 240 children were included in the morbidity and survival analyses (38 FN; 202 non-FN). No differences were found between FN and non-FN children in time from first presentation of symptoms to consultation with an oncology specialist or diagnosis, or number of hospital admissions / total days of admission for treatment complications. Overall survival was inferior for FN children in univariable analysis (P = 0.048) but not when risk group was included in a multivariable analysis (P = 0.15). No difference in event free survival or cumulative incidence of relapse was identified. CONCLUSION: The estimated incidence of childhood cancers in the Manitoba FN population is similar to provincial incidence rates. No differences in morbidity patterns or survival were found between Manitoba FN and non-FN children with cancer.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".