Clinical Benefit of 3 Tesla Magnetic Resonance Imaging Rescanning in Patients With Focal Epilepsy and Negative 1.5 Tesla Magnetic Resonance Imaging
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it