Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Epilepsy is a disorder of brain networks. A better understanding of structural and dynamic network properties may improve epilepsy diagnosis, treatment, and prognostics. Hubs are brain regions with high connectivity to other parts of the brain and are typically situated along the brain's most efficient communication pathways, supporting large-scale brain wiring and many higher order neural functions. The visualization and analysis of hubs offers a perspective on regional and global network organization and can provide novel insights into brain disorders and epilepsy. By notably supporting the interaction between various brain networks, hubs may be implicated in seizure spread and in epilepsy-related phenotypes. In this review, we will discuss the growing literature on atypical hub organization in common epilepsy syndromes, both related to neuroimaging of brain structure and function, and related to neurophysiological data from magneto- and electroencephalographic measures of neural dynamics. With studies increasingly exploring the clinical utility of network neuroscience approaches, we highlight the potential of hub mapping as a candidate biomarker of cognitive dysfunction and postsurgical seizure outcome. We will conclude the review with a discussion of current limitations and outlook for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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