P.089 Multiscale analysis of mesial temporal lobe epilepsy: Anatomo-Electrophysio-pathologic differentiation
Bibliographic record
Abstract
Background: Mesial temporal lobe epilepsy (mTLE) is a heterogenous condition with variable post-surgical outcomes. Combining high resolution magnetic resonance imaging (MRI), stereoelectroencephalography (SEEG) and histology may establish different subtypes of mTLE. Methods: Retrospective analysis of patients with mTLE with 1) SEEG Patterns 2) MRI 3) Post temporal lobectomy tissue analysis 4) Engel Classification. HippUnfold method was used to segment hippocampus on MRI. Results: Of 109 patients investigated with SEEG, 11 patients were analyzed so far. Low voltage fast activity was seen in 215 seizures, low-frequency periodic spikes in 21, sharp activity at <13 Hz in 58, rhythmic spike sharp wave activity in 86, and other types were less frequent. MRI revealed unilateral mesial temporal sclerosis (MTS) in 6 (54.55%), bilateral MTS in 2 (18.18%), and was normal in 3 (27.27%) patients. Histopathology showed ILAE grade I in 3 (37.5 %), II in 4 (50 %), IV in 1 (12.5%) patient. 63.63% had Engel Class I at 6 months. HippUnfold analysis and SEEG electrode coregistration was done in one patient and will be attempted in the rest. Conclusions: Our study highlights a strong correlation between SEEG findings and histological analysis in mTLE. A multidimensional classification will help predict long term outcomes.
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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) | 0.014 | 0.003 |
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".