P.106 Functional approach using intraoperative brain mapping and neurophysiological monitoring for surgery of arteriovenous malformations in eloquent areas
Bibliographic record
Abstract
Background: Surgical resection of arteriovenous malformations (AVMs) in eloquent areas is significantly associated with greater surgical morbidity. We describe a functional approach for surgical treatment of these lesions Methods: A total of 20 patients with AVMs in eloquent areas were surgically treated and retrospectively analyzed. Individualized functional approach, using brain mapping and/or neurophysiological monitoring was performed in each case according to every case specific features and location. Seventeen patients underwent surgery under assleep conditions and 3 -patients underwent awake intraoperative mapping Results: There was no mortality. Four patients had hemorrhagic complications (20%). Ten (50%) presented neurological immediate postoperative worsening. Eight of them achieved complete recovery in follow up and 2 showed a permanent deficit. At 6 months follow up all the patients (100%) had good clinical outcome (mRS less than 2). There were no intraoperative seizures but 5 patients (26.3%) developed postoperative seizures. Fifteen patients (75%) had total AVM resection. Language and/or motor function were identified in all but one patient (95%). Each case required changes in surgical strategy to preserve the motor and/or language functions during surgery. Conclusions: Intraoperative monitoring and brain mapping are valuable and safe for the treatment of eloquent AVMs by indentifying and protecting motor and language function during resection.
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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) | 0.004 | 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".