Practice guideline summary: Use of fMRI in the presurgical evaluation of patients with epilepsy
Bibliographic record
Abstract
OBJECTIVE: To assess the diagnostic accuracy and prognostic value of functional MRI (fMRI) in determining lateralization and predicting postsurgical language and memory outcomes. METHODS: An 11-member panel evaluated and rated available evidence according to the 2004 American Academy of Neurology process. At least 2 panelists reviewed the full text of 172 articles and selected 37 for data extraction. Case reports, reports with <15 cases, meta-analyses, and editorials were excluded. RESULTS AND RECOMMENDATIONS: The use of fMRI may be considered an option for lateralizing language functions in place of intracarotid amobarbital procedure (IAP) in patients with medial temporal lobe epilepsy (MTLE; Level C), temporal epilepsy in general (Level C), or extratemporal epilepsy (Level C). For patients with temporal neocortical epilepsy or temporal tumors, the evidence is insufficient (Level U). fMRI may be considered to predict postsurgical language deficits after anterior temporal lobe resection (Level C). The use of fMRI may be considered for lateralizing memory functions in place of IAP in patients with MTLE (Level C) but is of unclear utility in other epilepsy types (Level U). fMRI of verbal memory or language encoding should be considered for predicting verbal memory outcome (Level B). fMRI using nonverbal memory encoding may be considered for predicting visuospatial memory outcomes (Level C). Presurgical fMRI could be an adequate alternative to IAP memory testing for predicting verbal memory outcome (Level C). Clinicians should carefully advise patients of the risks and benefits of fMRI vs IAP during discussions concerning choice of specific modality in each case.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".