Biology and Clinical Management Challenges in Meningioma
Bibliographic record
Abstract
Meningiomas are the most frequently occurring intracranial tumors. They are characterized by a broad spectrum of histopathologic appearance. Molecular alterations driving meningioma development, which affect the NF2 gene, are found in roughly 50% of patients. Rare genetic events in benign meningiomas are mutations in TRAF7, KLF4, AKT1, and SMO; all of these mutations are exclusive of NF2 alterations. Progression to a clinically aggressive meningioma is linked to inactivation of CDKN2A/B genes, and a plethora of signaling molecules have been described as activated in meningiomas, which supports the concept of successful clinical use of specific inhibitors. Established treatments include surgical resection with or without radiotherapy delivered in a single fraction, a few large fractions (radiosurgery), or multiple fractions (fractionated radiotherapy). For recurrent and aggressive tumors, inhibitors of the vascular endothelial growth factor (VEGF) pathway, such as vatalinib, bevacizumab, and sunitinib, showed signs of activity in small, uncontrolled studies, and prospective clinical studies will test the efficacy of the tetrahydroisoquinoline trabectedin and of SMO and AKT1 inhibitors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".