P.079 Relationships between anatomical features and outcome after stereotactic laser amygdalohippocampotomy
Bibliographic record
Abstract
Background: Stereotactic laser amygdalohippocampotomy (SLAH) has recently been shown to be comparable to traditional temporal lobectomy procedures. The ideal extent and volume of laser ablations remains an area of investigation Methods: 65 patients treated with SLAH for MTS were considered in this retrospective study. Manual segmentations of ablations were created using post-procedure T1-MRI scans. Ablations were assessed in relation to whether they crossed the coronal plane of the superior lateral mesencephalic sulcus (LMS), the extent to which ablation crossed this landmark, and extent of ablation of the uncus. Analysis of was done with binary categorization of 12-month Engel classification score. Results: Distance of ablation posterior to the coronal plane of the LMS was not associated with better surgical outcome (Engel class 1: 6.32 ± 4.16 mm; Engel class 2-4: 7.93 ± 3.75mm; (p = 0.099)). Ratio of ablations extending posterior to the LMS was 0.82 (SD = .39) in Engel 1 patients, and 0.90 (SD = 0.3) in Engel 2-4 patients; (p = 0.370). Volume of ablation showed little correlation with outcome (Engel class 1: 6064 ± 2128 mm 3 ; Engel class 2-4: 5828 ± 3031 mm 3 ; (p=0.239)). Ablation of the uncus showed a strong association with better surgical outcome (Engel class 1: 0.71(SD = 0.31); Engel 2-4: 0.37 (SD = 0.36); p <0.001). Conclusions: Contrary to current practice, extension of ablation posterior to the LMS did not demonstrate improved 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 | 0.002 |
| 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) | 0.003 | 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".