Incidence of metastatic disease and survival among patients with newly diagnosed primary CNS tumors in the United States from 2004-2013
Bibliographic record
Abstract
Background: Population-based estimates of the incidence and prognosis of metastatic disease at the initial diagnosis of primary central nervous system (CNS) tumors are currently lacking. Methods: A total of 43,455 patients diagnosed with a primary CNS tumor were enrolled to evaluate metastatic rates utilizing the data from the Surveillance, Epidemiology, and End Results (SEER) program. We used multivariate logistic regression to analyze the risk factors associated with the presence of metastasis at the first visit of patients with metastatic medulloblastoma (MB), atypical teratoid/rhabdoid tumor (ATRT), glioblastoma multiforme (GBM), or pilocytic astrocytoma (PA). Hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer-specific death (CSD) of patients with these four CNS tumors were analyzed using multivariate Cox regression. Results: In patients with primary CNS embryonal tumors, the metastatic rates of patients with MB and ATRT were 14.51% and 19.25%, respectively. The metastatic rate for MB patients aged 0 to 18 years was 16.69%. In the patients with glioma, the metastatic rates of patients with PA and GBM were 1.55% and 1.39%, respectively. On multivariate logistic regression among patients with glioma, GBM (vs PA; OR, 2.12; 95% CI, 1.37 to 3.30; P=0.001) was associated with greater odds of having metastatic disease at diagnosis. On multivariate logistic regression among patients with GBM, MB, or ATRT, MB (vs GBM; OR, 4.66; 95% CI, 2.81 to 7.72; P<0.001) and ATRT (vs GBM; OR, 5.65; 95% CI, 3.27 to 9.75; P<0.001) were associated with greater odds of having metastatic disease at diagnosis. In the multivariate Cox proportional hazards model for CSD among patients with metastatic GBM or MB at diagnosis, gross total resection/total lobectomy (vs partial resection/partial lobectomy) was not related to a decreased or an increased risk of CSD. In patients with metastatic ATRT, compared to no surgery, gross total resection/total lobectomy or partial resection/partial lobectomy was not associated with a decreased risk of CSD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".