Meningioma: International Consortium on Meningiomas consensus review on scientific advances and treatment paradigms for clinicians, researchers, and patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Meningiomas are the most common primary intracranial tumors in adults and are increasing in incidence due to the aging population and increased access to neuroimaging. While most exhibit nonmalignant behavior, a subset of meningiomas are biologically aggressive and are associated with treatment resistance, resulting in significant neurologic morbidity and even mortality. In recent years, meaningful advances in our understanding of the biology of these tumors have led to the incorporation of molecular biomarkers into their grading and prognostication. However, unlike other central nervous system (CNS) tumors, a unified molecular taxonomy for meningiomas has not yet been established and remains an overarching goal of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy-Not Official World Health Organization (cIMPACT-NOW) working group. Additionally, clinical equipoise still remains on how specific meningioma cases and patient populations should be optimally managed. To address these existing gaps, members of the International Consortium on Meningiomas including field-leading experts, have prepared this comprehensive consensus narrative review directed toward clinicians, researchers, and patients. Included in this manuscript are detailed overviews of proposed molecular classifications, novel biomarkers, contemporary treatment strategies, trials on systemic therapies, health-related quality-of-life studies, and management strategies for unique meningioma patient populations. In each section, we discuss the current state of knowledge as well as ongoing clinical and research challenges to road map future directions for further investigation.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it