P.101 Conservative management of meningiomas with a moderate to high peritumoral brain edema index: a single-institution report
Bibliographic record
Abstract
Background: Meningiomas are the most commonly occurring benign intracranial tumors. When presenting with peritumoral brain edema (PTBE), surgical treatment can lead to patient morbidity. This retrospective case series aims to describe the conservative medical management of moderate to large meningiomas with large PTBE. Methods: Patients with suspected meningiomas greater than 2.0cm and edema index greater than 2.0 were identified by screening 3345 MRI scans between 2012-2017. Imaging analysis included MR imaging features of suspected meningiomas and clinical data was gathered from the electronic patient record (patient age, sex, patient symptoms, follow-up duration, and follow-up symptoms). Results: We report on 31 patients who received conservative medical management. Presenting complaints included headache, seizure, weakness; many presented asymptomatically. The average follow-up time was 3.96 years. At the final follow-up appointment, 19 (61%) patients were asymptomatic. Among symptomatic patients, seizures were the most common complaint. There was no mortality reported in our cohort and the average tumor progression was 7.04cm3/year. Conclusions: In this retrospective report of meningioma patients with high edema index, we found that most patients remained asymptomatic or had stable symptoms after at least 1-yr follow up after medical treatment. This study provides insight around the surgical decision-making for meningiomas with large spread of edema.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".