Incidence patterns for ependymoma: a Surveillance, Epidemiology, and End Results study
Bibliographic record
Abstract
OBJECT: Previous small studies disagree about which clinical risk factors influence ependymoma incidence. The authors analyzed a large, population-based cancer registry to examine the relationship of incidence to patient age, sex, race, and tumor location, and to determine incidence trends over the past 3 decades. METHODS: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER-9) study, which was conducted from 1973 to 2003. Histological codes were used to define ependymomas. Age-adjusted incidence rates were compared by confidence intervals in the SEER*Stat 6.2 program. Multiplicative Poisson regression and Joinpoint analysis were used to determine annual percentage change and to look for sharp changes in incidence, respectively. RESULTS: From the SEER database, 1402 patients were identified. The incidence rate per 100,000 person-years was significantly higher in male than in female patients (males 0.227 +/- 0.029, females 0.166 +/- 0.03). For children, the age at diagnosis differed significantly by tumor location, with the mean age for patients with infratentorial tumors calculated as 5 +/- 0.4 years; for supratentorial tumors it was 7.77 +/- 0.6 years, and for spinal lesions it was 12.16 +/- 0.8 years. (Values are expressed as the mean +/- standard error [SE].) Adults showed no difference in the mean age of incidence by location, although most tumors in this age group were spinal. Between 1973 and 2003, the incidence increased significantly among adults but not among children, and there were no sharp changes at any single year, both before and after age adjustment. CONCLUSIONS: Males have a higher incidence of ependymoma than do females. A biological explanation remains elusive. Ependymoma occurs within the CNS at distinct locations at different ages, consistent with hypotheses postulating distinct populations of radial glial stem cells within the CNS. Ependymoma incidence appears to have increased over the past 3 decades, but only in adults.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".