MNGO-18. MANAGEMENT OF MULTIPLE MENINGIOMAS
Bibliographic record
Abstract
Multiple meningiomas are sporadic, radiation-induced or familial and account for 1-10% of meningiomas. Their management is more challenging from single tumors as patients may require multiple treatments. OBJECTIVE: To describe the management of patients with multiple intracranial meningiomas. We reviewed our practice on the management for patients with two or more spatially separated synchronous or metachronous meningiomas. Patients with neurofibromatosis type 2 were excluded. We collected clinical, imaging and treatment data in order to analyze epidemiological aspects of the disease and outcomes of active treatment and surveillance. Over the last 25 years we managed 133 consecutive patients, with total of 395 synchronous and 53 metachronous meningiomas. 106 patients had sporadic disease, 26 radiation-induced and 1 had familial menigniomatosis. In 66 cases the tumors were initially asymptomatic. At presentation, about half of the patients had two tumors, 67% were small (≤2cm) and 11% were large (>4cm). Fifty-four patients had up front treatment and 18 of them underwent treatment of a different meningioma after a mean of 5 years. Thirty-one patients required treatment during surveillance, on average 4 years after diagnosis. Overall, 64% of the patients had treatment for 142 tumors; 67 with surgery and 18 only with radiotherapy. One in four patients had two or more meningiomas treated. The mean follow-up period was 7 years and 13% of the treated patients had salvage therapy. Multiple meningiomas are often asymptomatic, probably because two thirds of them are small tumors. About two thirds of patients with multiple meningiomas require treatment, two thirds up front and one third during surveillance. However, two thirds of meningiomas do not require active treatment. We recommend treatment for the symptomatic tumors, or those that grow at a rate that may cause symptoms during the patient’s expected lifespan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".