Bibliographic record
Abstract
Epilepsy represents a multifaceted group of disorders divided into two broad categories, partial and generalized, based on the seizure onset zone. The identification of the neuroanatomic site of seizure onset depends on delineation of seizure semiology by a careful history together with video-EEG, and a variety of neuroimaging technologies such as MRI, fMRI, FDG-PET, MEG, or invasive intracranial EEG recording. Temporal lobe epilepsy (TLE) is the commonest form of focal epilepsy and represents almost 2/3 of cases of intractable epilepsy managed surgically. A history of febrile seizures (especially complex febrile seizures) is common in TLE and is frequently associated with mesial temporal sclerosis (the commonest form of TLE). Seizure auras occur in many TLE patients and often exhibit features that are relatively specific for TLE but few are of lateralizing value. Automatisms, however, often have lateralizing significance. Careful study of seizure semiology remains invaluable in addressing the search for the seizure onset zone.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".