Global incidence of malignant brain and other central nervous system tumors by histology, 2003–2007
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it