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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".