Neuroinflammation imaging markers for epileptogenesis
Bibliographic record
Abstract
Epilepsy can be a devastating disorder. In addition to debilitating seizures, epilepsy can cause cognitive and emotional problems with reduced quality of life. Therefore, the major aim is to prevent the disorder in the first place: identify, detect, and reverse the processes responsible for its onset, and monitor and treat its progression. Epilepsy often occurs following a latent period of months to years (epileptogenesis) as a consequence of a brain insult, such as head trauma, stroke, or status epilepticus. Although this latent period clearly represents a therapeutic window, we are not able to stratify patients at risk for long-term epilepsy, which is prerequisite for preventative clinical trials. Moreover, because of the length of the latent period, an early biomarker for treatment response would be of high value. Finally, mechanistic biomarkers of epileptogenesis may provide more profound insight in the process of disease development.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".