ILAE Neuroimaging Task Force highlight: Review MRI scans with semiology in mind
Bibliographic record
Abstract
The ILAE Neuroimaging Task Force aims to publish educational case reports highlighting basic aspects related to neuroimaging in epilepsy consistent with the educational mission of the ILAE. It is important to obtain MRI scans early in the clinical course of epilepsy, using an optimized protocol. Furthermore, it is critical that MRI scans are reviewed by experts who have been provided with all the clinical information and results from other investigations. We report a patient with a 21-year history of drug-resistant seizures who was admitted from another centre for presurgical evaluation. She had four previous MRI scans from this centre which were reported as unremarkable. However, a review of the MRI scan obtained on the day of admission, with the patient's ictal semiology in mind, resulted in identification of an epileptogenic lesion which was later confirmed by video-EEG monitoring and interictal PET. This lesion was present on all previous MRI scans and showed no change. The patient underwent lesionectomy, and histopathology of the resected specimen was consistent with a dysembryoplastic neuroepithelial tumour. The patient remains seizure-free, 2.5 years after surgery. This case highlights the importance of obtaining detailed descriptions of seizure semiology and considering them when reviewing MR images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".