Refractory Epilepsy: The Role of Positron Emission Tomography
Bibliographic record
Abstract
RATIONALE: Presurgical localization of the epileptogenic focus is critical to successful surgery. Traditionally, localization of the epileptogenic focus depends on seizure semiology, scalp video-electroencephalography (vEEG), magnetic resonance imaging (MRI), neuropsychological assessment, and, when needed, intracranial EEG (iEEG). We aimed to explore the role of positron emission tomography (PET) in the presurgical evaluation of patients with refractory epilepsy. METHODS: A retrospective review was conducted on patients from London Health Sciences Centre (London, Ontario) with refractory epilepsy who underwent PET from September of 2011 to April of 2016. The accuracy of epileptogenic focus localization was compared between different investigative modalities (MRI, vEEG, iEEG, PET), and the outcomes were documented, including seizure freedom after surgical resection, improvement of seizure frequency, guidance for further investigations, and exclusion of patients from further evaluation. Patients who underwent surgery were followed up at 3 months and onward. RESULTS: We identified 62 patients with refractory epilepsy who underwent PET. The mean age was 34 years (range=20-68). A total of 36 had concordant PET and vEEG findings: 6 had surgical resection and either became seizure-free (29.4%) or had improvement in seizure frequency (5.9%) at 3 months; 11 had surgical resection and either became seizure-free (29.4%) or had improvement in seizure frequency (35.3%) at 3 months, but required iEEG for final verification. CONCLUSIONS: PET has an important role in presurgical evaluation of patients with refractory epilepsy. It may allow resection of the epileptogenic focus without the need for iEEG, guiding intracranial electrode placement for further localization of the epileptogenic focus, or exclusion of patients from further evaluation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".