Incidence Patterns of Central Nervous System Germ Cell Tumors
Bibliographic record
Abstract
BACKGROUND: Incidence patterns of central nervous system (CNS) germ cell tumors (GCTs) have been reported, but the influence of underlying host risk factors has not been rigorously explored. We aimed to determine in a large, population-based cancer registry how age, sex, and race, influence the occurrence of CNS GCTs in the pediatric population. METHODS: Using the Surveillance, Epidemiology, and End Results registry, we identified cases of histologically confirmed GCTs in children, adolescents, and young adults (age 0 to 29 y), diagnosed between 1973 and 2004. The cases were limited to only those with the International Classification of Childhood Cancer Xa: intracranial and intraspinal germ-cell tumors. Incidence rates (per 10,000) for each sex and race were plotted for single-age groups, and then stratified by tumor location and pathology subtype. RESULTS: The sample included a total of 638 cases (490 males). Males had significantly higher rates of CNS GCTs than females. Male and female rates diverged significantly starting at the age of 11 years and remained widely discrepant until the age of 30 years. There were more germinomas than nongerminomas in both sexes. Germinomas peaked in incidence during adolescence, whereas nongerminoma incidence remained relatively constant in children and young adults. Tumor location differed strikingly by sex (P<0.0001) with pineal location more common in males (61.0% vs. 15.5%). Asian race was associated with a higher rate of CNS GCTs than other races. CONCLUSIONS: Males have higher incidence of CNS GCTs, primarily germinomas, than females, starting in the second decade. Pineal location is strongly associated with male sex, with pineal germinomas representing over half of all CNS GCTs in males. Asian-Americans have higher rates than other races. These findings suggest a robust but poorly understood influence of sex, either genetic or hormonal, and race on the occurrence of CNS GCTs.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".