Management of multiple meningiomas
Bibliographic record
Abstract
OBJECTIVE Multiple meningiomas account for 1%-10% of meningiomas. This study describes epidemiological aspects of the disease and its management, which is more challenging than for single tumors. METHODS A consecutive series of adult patients with ≥ 2 spatially separated meningiomas was reviewed. Patients with neurofibromatosis Type 2 were excluded. The authors collected clinical, imaging, histological, and treatment data to obtain information on epidemiology, management options, and outcomes of active treatment and surveillance. RESULTS A total of 133 consecutive patients were included over 25 years, with a total of 395 synchronous and 53 metachronous meningiomas, and a median of 2 tumors per patient. One hundred six patients had sporadic disease, 26 had radiation-induced disease, and 1 had familial meningiomatosis. At presentation, half of the patients were asymptomatic. In terms of their maximum cross-sectional diameter, the tumors were small (≤ 2 cm) in 67% and large (> 4 cm) in 11% of the meningiomas. Fifty-four patients had upfront treatment, and 31 had delayed treatment after an observation period (mean 4 years). One in 4 patients had ≥ 2 meningiomas treated. Overall, 64% of patients had treatment for 142 tumors-67 with surgery and 18 with radiotherapy alone. The mean follow-up was 7 years, with 13% of treated patients receiving salvage therapy. Approximately 1 in 4 patients who underwent surgery had ≥ 1 WHO Grade II or III meningioma. Meningiomas of different histological subtypes and grades in the same patient were not uncommon. CONCLUSIONS Multiple meningiomas are often asymptomatic, probably because the majority are small and a significant proportion are induced by radiation. Approximately two-thirds of patients with multiple meningiomas require therapy, but only one-third of all meningiomas need active treatment. The authors recommend surveillance for stable and asymptomatic meningiomas and therapy for those that are symptomatic or growing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".