Global incidence of malignant brain and other central nervous system tumors by histology, 2003–2007
Bibliographic record
Abstract
BACKGROUND: Previous reports have shown that overall incidence of malignant brain and other central nervous system (CNS) tumors varied significantly by country. The aim of this study was to estimate histology-specific incidence rates by global region and assess incidence variation by histology and age. METHODS: Using data from the Central Brain Tumor Registry of the United States (CBTRUS) and the International Agency for Research on Cancer's (IARC) Cancer Incidence in Five Continents X (including over 300 cancer registries), we calculated the age-adjusted incidence rates (AAIR) per 100000 person-years and 95% CIs for brain and other CNS tumors overall and by age groups and histology. RESULTS: There were significant differences in incidence by region. Overall incidence of malignant brain tumors per 100000 person-years in the US was 5.74 (95% CI = 5.71-5.78). Incidence was lowest in Southeast Asia (AAIR = 2.55, 95% CI = 2.44-2.66), India (AAIR = 2.85, 95% CI = 2.78-2.93), and East Asia (AAIR = 3.07, 95% CI = 3.02-3.12). Incidence was highest in Northern Europe (AAIR = 6.59, 95% CI = 6.52-6.66) and Canada (AAIR = 6.53, 95% CI = 6.41-6.66). Astrocytic tumors showed the broadest variation in incidence regionally across the globe. CONCLUSION: Brain and other CNS tumors are a significant source of cancer-related morbidity and mortality worldwide. Regional differences in incidence may provide clues toward genetic or environmental causes as well as a foundation for broadening knowledge of their epidemiology. Gaining a comprehensive understanding of the epidemiology of malignant brain tumors globally is critical to researchers, public health officials, disease interest groups, and clinicians and contributes to collaborative efforts in future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".