The Central Brain Tumor Registry of the United States Histopathological Grouping Scheme Provides Clinically Relevant Brain and Other Central Nervous System Categories for Cancer Registry Data.
Bibliographic record
Abstract
Background: Brain and other central nervous system (CNS) tumors are a heterogenous collection of tumors, but they are generally reported in local and national cancer statistics as a single, large category. Although the collection of non-malignant brain and other CNS tumors has been mandated since diagnosis year 2004, these tumors are often excluded from standard statistical reports on cancer despite their burden on populations in the United States and Canada. The Central Brain Tumor Registry of the United States (CBTRUS) historical and current histopathological grouping schemes have been developed in collaboration with neuropathologists to capture the diversity of these tumors in clinically relevant categories. The goal of this analysis was to test a new recode variable based on the CBTRUS histopathology grouping prior to releasing the variable for use in the North American Association of Central Cancer Registries (NAACCR) Cancer in North American (CiNA) data sets and by individual cancer registries. Methods: The CBTRUS histopathology grouping scheme variable was created and implemented in an evaluation CiNA data set. The accuracy of the variable's categories was evaluated. Counts and incidence rates were calculated using SEER*Stat. Results: 3rd edition [ICD-O-3]) while about 71% were nonmalignant (ICD-O-3 behavior code /0 or /1). The overall age-adjusted annual incidence rate (AAAIR) of brain and other CNS tumors was 24.44 per 100,000 (95% CI, 24.37-24.51). The most common histopathologies were meningioma, of which approximately 99% were nonmalignant (AAAIR, 9.09 per 100,000; 95% CI, 9.05-9.13); tumors of the pituitary, of which about 99% were nonmalignant (AAAIR, 4.28 per 100,000; 95% CI, 4.25-4.31); and glioblastoma, of which 100% were malignant behavior (AAAIR, 3.20 per 100,000; 95% CI, 3.18-3.22). Conclusion: Brain and other CNS tumors make up an extremely diverse category that contributes substantially to the cancer burden in North America. The CBTRUS histopathology grouping variable provides clinically relevant groupings for analysis of these tumors in the NAACCR CiNA as well as by individual central cancer registry groups. We encourage the use of this variable to support more detailed analysis of this important group of tumors.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".