Case-Completeness of Nonmalignant Central Nervous System Tumors in the Canadian Cancer Registry, 2011-2015.
Bibliographic record
Abstract
INTRODUCTION: The public health burden of nonmalignant central nervous system tumors (NMCNSTs) in Canada is unclear because casefinding and registration have historically been incomplete. The primary objective of this study is to quantify case-completeness of NMCNSTs in the Canadian Cancer Registry (CCR) using US Surveillance, Epidemiology and End Results Program (SEER) rates as the standard. METHODS: Counts, distributions, and age-standardized incidence rates (ASIRs) for malignant central nervous system tumors (MCNSTs) and NMCNSTs by sex, age, site, histology, tumor size, World Health Organization (WHO) grade, and year of diagnosis were estimated for the United States and Canada (excluding Quebec) for the time period 2011-2015 using SEER and CCR data, respectively. Canadian and provincial standardized incidence ratios (SIRs) were also calculated by sex, age, site, histology and year of diagnosis using SEER rates as the standard. Under the assumptions of high NMCNST case-completeness in SEER registries and comparable population-based rates in the United States and Canada, SIRs less than 100% suggest incomplete case registration. RESULTS: Between 2011 and 2015, the ASIR for MCNSTs is similar in the United States (6.97 per 100,000 persons; 95% CI, 6.89-7.05), Canada (7.11 per 100,000; 95% CI, 6.97-7.24), and across provinces (range, 6.53-7.35 per 100,000). Conversely, the ASIR for NMCNSTs is 1.61 times greater in the United States (17.15 per 100,000; 95% CI, 17.02-17.27) than Canada (10.65 per 100,000; 95% CI, 10.49-10.82). SIRs for NMCNSTs range from 22.5% (95% CI, 15.6%-31.5%) in Prince Edward Island to 85.3% (95% CI, 83.7%-86.9%) in Ontario and vary by demographics, tumor characteristics, and year. Identified data limitations include nonspecific tumor characteristics and potential misclassification. CONCLUSION: NMCNST surveillance in Canada is compromised by incomplete case registration and data quality limitations. Enhancement of case ascertainment processes for these tumors, which may be diagnosed radiologically, may be warranted.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".