1.5 versus 3 Tesla structural MRI in patients with focal epilepsy
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Structural MRI is a critical component in the pre-surgical investigation of epilepsy, as identifying an epileptogenic lesion increases the chance of post-surgical seizure freedom. In general practice, 1.5T and 3T MRI scans are still the mainstream in most epilepsy centres, particularly in resource-poor countries. When 1.5T MRI is non-lesional, a repeat scan is often performed as a higher-field structural scan, usually 3T. However, it is not known whether scanning at 3T increases diagnostic yield in patients with focal epilepsy. We sought to compare lesion detection and other features of 1.5T and 3T MRI acquired in the same patients with epilepsy. METHODS: MRI scans (1.5T and 3T) from 100 patients were presented in a blinded, randomized order to two neuroradiologists. The presence, location, and number of potentially epileptogenic lesions were compared. In addition, tissue contrast and the presence of motion/technical artifacts were compared using a 4-point subjective scale. RESULTS: Both the qualitative tissue contrast and motion/technical artifacts were improved at 3T. However, this did not result in statistically significant improvement in lesion detection. Qualitatively, five patients had subtle lesions seen only at 3T. However, minor differences in image acquisition parameters between 1.5T and 3T scans in these cases may have resulted in greater lesion visibility at 3T in four patients. Based on a general linear model analysis, the presence of a focal abnormality on EEG was predictive of the presence of a lesion at 1.5T and 3T. SIGNIFICANCE: Repeat MRI scanning of patients with focal epilepsy at 3T using similar scan protocols does not significantly increase diagnostic yield over scanning at 1.5T; the increased signal-to-noise ratio can potentially be better allocated for novel scan sequences in order to provide more clinical value.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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