Management of cavernous sinus meningiomas: Consensus statement on behalf of the EANS skull base section
Bibliographic record
Abstract
Introduction: The evolution of cavernous sinus meningiomas (CSMs) might be unpredictable and the efficacy of their treatments is challenging due to their indolent evolution, variations and fluctuations of symptoms, heterogeneity of classifications and lack of randomized controlled trials. Here, a dedicated task force provides a consensus statement on the overall management of CSMs. Research question: To determine the best overall management of CSMs, depending on their clinical presentation, size, and evolution as well as patient characteristics. Material and methods: Using the PRISMA 2020 guidelines, we included literature from January 2000 to December 2020. A total of 400 abstracts and 77 titles were kept for full-paper screening. Results: The task force formulated 8 recommendations (Level C evidence). CSMs should be managed by a highly specialized multidisciplinary team. The initial evaluation of patients includes clinical, ophthalmological, endocrinological and radiological assessment. Treatment of CSM should involve experienced skull-base neurosurgeons or neuro-radiosurgeons, radiation oncologists, radiologists, ophthalmologists, and endocrinologists. Discussion and conclusion: Radiosurgery is preferred as first-line treatment in small, enclosed, pauci-symptomatic lesions/in elderly patients, while large CSMs not amenable to resection or WHO grade II-III are candidates for radiotherapy. Microsurgery is an option in aggressive/rapidly progressing lesions in young patients presenting with oculomotor/visual/endocrinological impairment. Whenever surgery is offered, open cranial approaches are the current standard. There is limited experience reported about endoscopic endonasal approach for CSMs and the main indication is decompression of the cavernous sinus to improve symptoms. Whenever surgery is indicated, the current trend is to offer decompression followed by radiosurgery.
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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 | 0.067 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| 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".