Seizure Outcome after Resection of Supratentorial Cavernous Malformations: A Study of 168 Patients
Bibliographic record
Abstract
PURPOSE: The optimal management of cerebral cavernous malformations (CCMs) with epileptic seizures is still a matter of debate. The aim of our study was to examine seizure outcome in the largest published series of surgically treated patients with epilepsy due to a supratentorial CCM, and to define predictors for good surgical outcome. METHODS: We retrospectively studied 168 consecutive patients with a single supratentorial CCM and symptomatic epilepsy in a multicenter study. Pre- and postoperative clinical examinations, age at epilepsy onset, age at operation, type of symptoms due to the CCM (seizures, headache, hemorrhage, focal deficits), type and frequency of epileptic seizures, and the localization and size of the CCM were assessed. Seizure outcome was determined in the first, second, and third postoperative years. RESULTS: The CCM was completely resected in all patients. More than two thirds of the patients were classified as seizure free in the first 3 postoperative years. Predictors for good seizure outcome were age older than 30 years at the time of surgery, mesiotemporal CCM localization, CCM size <1.5 cm, and the absence of secondarily generalized seizures. No mortality occurred in our series, but only mild postoperative neurologic deficits in 12 (7%) patients. CONCLUSIONS: Considering the natural history of CCMs, the favorable neurologic and seizure outcome, surgical resection of CCMs should be considered in all patients with supratentorial CCMs and concomitant epilepsy, irrespective of the presence or absence of predictors for a favorable seizure outcome.
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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 | 0.000 | 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) | 0.001 | 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".