Risk factors for glioblastoma are shared by other brain tumor types
Bibliographic record
Abstract
The reported risk factors for glioblastoma (GBM), i.e., ionizing radiation, Li-Fraumeni syndrome, Neurofibromatosis I, and Turcot syndrome, also increase the risk of other brain tumor types. Risk factors for human GBM are associated with different oncogenic mutation profiles. Pedigreed domestic dogs with a shorter nose and flatter face (brachycephalic dogs) display relatively high rates of glioma formation. The genetic profiles of canine gliomas are also idiosyncratic. The association of putatively different mutational patterns in humans and canines with GBM suggests that different oncogenic pathways can result in GBM formation. Strong epidemiological evidence for an association between exposure to chemical carcinogens and an increased risk for development of GBM is currently lacking. Ionizing radiation induces point mutations, frameshift mutations, double-strand breaks, and chromosomal insertions or deletions. Mutational profiles associated with chemical exposures overlap with the broad mutational patterns seen with ionizing radiation. Weak statistical associations between chemical exposures and GBM reported in epidemiology studies are biologically plausible. Molecular approaches comparing reproducible patterns seen in spontaneous GBM with analogous patterns found in GBMs resected from patients with known significant exposures to potentially carcinogenic chemicals can address difficulties presented by traditional exposure assessment.
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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) | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".