Unveiling epileptogenic lesions: The contribution of image processing
Bibliographic record
Abstract
Magnetic resonance imaging (MRI) is a pivotal component in the investigation of patients with any form of epilepsy because of its unmatched ability in visualizing structural brain pathology. The MRI signature of newly diagnosed epilepsy is not yet fully defined, mainly because of the lack of a cohesive methodology to evaluate structural changes in the early stages of the disease. By revealing subtle lesions that previously eluded visual inspection in patients with drug-resistant epilepsy, quantitative computer-assisted image analysis has clearly demonstrated increased sensitivity and diagnostic accuracy compared to conventional techniques. Therefore, the application of image processing methods in patients with newly diagnosed epilepsy promises to reduce the trial-error period in potential surgical candidates, provide solid biomarkers for monitoring the disease and identifying treatment responders. A clearer understanding of brain pathology at the early stages of the disorder will help clinicians to develop better criteria for identifying patients who are at risk of secondary brain damage and for timely intervention that achieves seizure control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".