Retinoblastoma Incidence Trends in Canada: A National Comprehensive Population-Based Study
Bibliographic record
Abstract
PURPOSE: To determine the incidence rates and geographic distribution of retinoblastoma in Canada to aid cancer control programs. METHODS: Patients with retinoblastoma whose data were available from the Canadian Cancer Registry (CCR) and Le Registre Québécois du Cancer (LRQC) were studied. Using third edition International Classification of Diseases for Oncology (ICD-O) codes, the authors examined the incidence rates and geographic distribution of patients with retinoblastoma between 1992 and 2010. Patient data including sex, age, and laterality of the retinoblastoma were analyzed. RESULTS: Between 1992 and 2010 in Canada, the average annual incidence rate of retinoblastoma was 11.58 cases per 1 million children younger than 5 years (95% CI [confidence interval]: 10.48 to 12.76). The incidence rate was stable over time, with an average age at diagnosis of 2.30 ± 6.85 years and no gender predilection. The laterality of the reported cases was 81.48% for uni-lateral cases and 18.52% for bilateral cases. Provincially, Nova Scotia had twice the national average and the highest incidence rates of retinoblastoma across the Canadian provinces. CONCLUSIONS: This is the first study to define the disease burden of retinoblastoma and to highlight important longitudinal, geographic, and spatial differences in the distribution of retinoblastoma in Canada between 1992 and 2010. The results of this study indicate continuity of clinical trends between Canada, the United States, and other developed countries. [J Pediatr Ophthalmol Strabismus. 2019;56(2):124-130.].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| 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".