Bibliographic record
Abstract
Epilepsy is among the most prevalent neurologic disorders worldwide, and one-third of patients continue to experience seizures despite medical therapy. Many of these pharmacoresistant patients suffer from temporal lobe epilepsy (TLE). High-resolution MRI has been instrumental to diagnose mesiotemporal sclerosis, the hallmark pathology of TLE, and for mapping distributed substrates of the condition.1 These methods have shaped our understanding of TLE as a network disorder, highlighting how structural changes and brain rewiring beyond the mesiotemporal disease epicenter may affect network signaling. This perspective has granted a powerful framework to study seizure mechanisms and to better understand the unique phenotype of TLE. Indeed, TLE is more than a seizure disorder: Over 50% of patients present with clinically significant neuropsychological impairment,2 with degrees of dysfunction generally mirroring the extent of network compromise.3 Given how cognitive impairment often affects patient functioning, quality of life, and well-being,4 there is a pressing need to identify factors able to mitigate atypical brain network organization in TLE.
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.
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".