International recommendation for a comprehensive neuropathologic workup of epilepsy surgery brain tissue: A consensus Task Force report from the <scp>ILAE</scp> Commission on Diagnostic Methods
Bibliographic record
Abstract
Epilepsy surgery is an effective treatment in many patients with drug-resistant focal epilepsies. An early decision for surgical therapy is facilitated by a magnetic resonance imaging (MRI)-visible brain lesion congruent with the electrophysiologically abnormal brain region. Recent advances in the pathologic diagnosis and classification of epileptogenic brain lesions are helpful for clinical correlation, outcome stratification, and patient management. However, application of international consensus classification systems to common epileptic pathologies (e.g., focal cortical dysplasia [FCD] and hippocampal sclerosis [HS]) necessitates standardized protocols for neuropathologic workup of epilepsy surgery specimens. To this end, the Task Force of Neuropathology from the International League Against Epilepsy (ILAE) Commission on Diagnostic Methods developed a consensus standard operational procedure for tissue inspection, distribution, and processing. The aims are to provide a systematic framework for histopathologic workup, meeting minimal standards and maximizing current and future opportunities for morphofunctional correlations and molecular studies for both clinical care and research. Whenever feasible, anatomically intact surgical specimens are desirable to enable systematic analysis in selective hippocampectomies, temporal lobe resections, and lesional or nonlesional neocortical samples. Correct orientation of sample and the sample's relation to neurophysiologically aberrant sites requires good communication between pathology and neurosurgical teams. Systematic tissue sampling of 5-mm slabs along a defined anatomic axis and application of a limited immunohistochemical panel will ensure a reliable differential diagnosis of main pathologies encountered in epilepsy surgery.
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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 | 0.092 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.014 | 0.007 |
| Research integrity | 0.024 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".