Clinical Benefit of 3 Tesla Magnetic Resonance Imaging Rescanning in Patients With Focal Epilepsy and Negative 1.5 Tesla Magnetic Resonance Imaging
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging is an essential tool in the pre-surgical evaluation of patients with drug-resistant epilepsy. OBJECTIVE: Our aim was to assess the value of re-imaging patients with focal drug-resistant epilepsy. METHODS: Thirty patients with negative or non-conclusive 1.5 Tesla magnetic resonance imaging were rescanned with 1.5T and 3T. All of them had previous 1.5 scans with no seizure protocol in a non-specialized center. Two neuroradiologists who were blinded to prior imaging results randomly reviewed the magnetic resonance images. Kappa score was used to assess the reliability. RESULTS: Mean age of patients was 30 (SD ± 11) years. The intra-observer agreement for the first radiologist was 0.74 for 1.5T and 0.71 for 3T. In the second radiologist it was 0.82 and 0.66, respectively. Three lesions (10%) were identified by general radiologists in non-specialized centers using a 1.5T standard protocol. In our center a consensus between two neuroradiologists using epilepsy protocol identified seven lesions (23%) using 1.5T and 10 (33%) using 3T (p < 0.01). In 28% of patients this additional information resulted in a change in clinical management. CONCLUSIONS: 3T magnetic resonance imaging rescanning improves the diagnostic yield in patients with focal epilepsy and previous negative 1.5T magnetic resonance imaging. Use of 3T magnetic resonance imaging, epilepsy protocols, and interpretation by experienced neuroradiologists is highly recommended.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".