Stereotactic Radiosurgery for Intracranial Meningiomas
Bibliographic record
Abstract
Meningiomas are among the most common adult brain tumors. Although the optimal management of meningiomas would provide complete elimination of the lesion, this cannot always be accomplished safely through resection. Therefore, other therapeutic modalities, such as stereotactic radiosurgery (as primary or adjunctive therapy), have emerged. In the current review, we have provided an overview of the historical outcomes of various radiosurgical modalities applied in the management of meningiomas. Furthermore, we provide a discussion on key factors (eg World Health Organization grade, lesion size, and lesion location) that affect tumor control and adverse event rates. We discuss recent changes in our understanding of meningiomas, based on molecular and genetic markers, and how these will change our perspective on the management of meningiomas. We conclude by outlining the areas in which knowledge gaps persist and provide suggestions as to how these can be addressed. ABBREVIATIONS: ARE, adverse radiation event CI, conformity index FSRT, fractionated radiotherapy GK, Gamma Knife GTR, gross total resection HGM, high-grade meningioma LINAC, linear accelerator NF2, neurofibromatosis type 2 OA, optic apparatus OS, overall survival PFS, progression-free survival SRS, stereotactic radiosurgery STR, subtotal resection TC, tumor control VS, vestibular schwannoma WHO, World Health Organization
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".