<scp>ILAE</scp> neuroimaging task force highlight: The utility of multimodal neuroimaging in diagnostic and presurgical workup of drug‐resistant focal epilepsy
Bibliographic record
Abstract
The ILAE Neuroimaging Task Force publishes educational case reports that highlight basic aspects of neuroimaging in epilepsy, consistent with ILAE's educational mission. In patients with drug-resistant focal epilepsy who are candidates for surgical intervention, the identification of structural abnormalities is a strong predictor of favorable postoperative seizure outcomes. When conventional imaging is insufficient, the integration of multimodal neuroimaging data with structural, metabolic, and functional imaging modalities is often helpful. The following two illustrative cases from two different centers highlight the challenges and needs to integrate the information from multiple imaging modalities for a more accurate diagnosis and resection planning of drug-resistant focal epilepsies. This approach can increase the number of patients eligible for surgery while minimizing the risk of postoperative deficits.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Educational case report on multimodal neuroimaging in presurgical epilepsy workup.
It presents educational epilepsy cases about neuroimaging practice, not research practice.
Educational clinical neuroimaging case reports for epilepsy care, not research practice as object.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".