Optimizing Epilepsy Surgery with Intraoperative MR Imaging
Bibliographic record
Abstract
PURPOSE: The surgical treatment of medically intractable temporal lobe epilepsy includes the resection of temporal lobe structures. Although the reported seizure-free outcomes are highly variable, there is growing evidence that the extent of resection of the mesiotemporal lobe directly correlates with seizure control. METHODS: A moveable, high-field intraoperative magnetic resonance (MR) system was used to monitor and optimize the resection of the amygdala and hippocampus in 14 epilepsy patients. Fourteen patients with intractable seizures of temporal lobe origin underwent standard preoperative investigations including MR imaging, EEG telemetry, single-photon emission computed tomography, and neuropsychologic and sodium amytal testing. Anterior temporal lobectomy was performed on 10 patients, whereas four were treated with selective amygdalohippocampectomy. Intraoperative electrocorticography was applied as required. For all procedures, the objective was to resect the amygdala completely, and hippocampus to the posterior margin of the brainstem. RESULTS: Interdissection intraoperative MR imaging taken when optimal resection was thought to have been achieved revealed residual unresected amygdala or hippocampus in seven of 14 patients. An unexpected acute hematoma was found in one patient. At 17 months' follow-up, 13 (93%) of 14 patients are seizure free or have significantly improved seizure control. CONCLUSIONS: The mobile high-field intraoperative MR system provides high-resolution images without restriction on surgical instruments or techniques. The ability to identify and resect residual mesial temporal lobe targets before craniotomy closure is of potentially tremendous value in optimizing seizure control.
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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 | 0.000 | 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) | 0.001 | 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".