3. Characteristics of Glioblastoma in Latino Americans
Bibliographic record
Abstract
Latino Americans are a rapidly growing ethnic group in the United States. The characteristics of glioblastoma in this population is poorly studied. We have evaluated the data of 47,540 glioblastoma patients from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. This SEER data from 1973-2000 includes up to 13 cancer registries. For 2001 to 2011, the data has improved geographic coverage with 18 registries encompassing 28% of the U.S. population. Latinos have a lower incidence of GBM than non-Latino Whites. Gender distribution is similar. The total SEER data show that Latinos present slightly younger and have a higher incidence of giant cell glioblastoma and gliosarcoma than non-Latino Whites. Despite higher rates of radiation therapy, the one year survival rate (34.7%) for non-Latino White populations is less than for Latinos (39.0%, p <0.001). Subset analyses (2001-2011) of all the above parameters show similar results except for gliosarcoma incidence. A literature search does not identify MGMT or IDH1 data regarding Latino Americans. We have assessed 2 prognostic markers in 30 Latino glioblastoma patients. MGMT methylation is present in 24% and IDH1 mutation is found in 12.5%. Our preliminary data suggests that Latinos may have a greater incidence of MGMT unmethylated tumors. Younger age may possibly contribute to improved survival in Latinos but the underlying molecular basis is unresolved.
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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 | 0.002 |
| 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) | 0.002 | 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".