[Temporal epilepsy: clinical, diagnostic and therapeutic aspects].
Bibliographic record
Abstract
INTRODUCTION: Epilepsy is a common neurological disease with significant public health impact. Temporal lobe epilepsy (TLE) is one of the most frequent and complex epilepsies. AIM: To review the most important aspects of clinical diagnosis, the use of diagnostic tests, and medical and surgical treatment of TLE. DEVELOPMENT: TLE is a well described type of epilepsy. Approximately 40% of patients with TLE develop drug resistant epilepsy. Nowadays, new diagnosis tools have been added for the assessment of patients with TLE that could be candidates for epilepsy surgery. However semiology remains the starting point for the disease understanding and every localizing and lateralizing sign is relevant to understand the potential onset and dissemination of seizures. The combination of tests and semiology is important to decide which cases require intracranial electrodes. The prognosis of epilepsy surgery in TLE is very good and between 60 to 70% of patients rendered seizure free after surgery. CONCLUSIONS: The approach to patients with TLE is complex and requires a multidisciplinary team of trained specialists.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.012 | 0.007 |
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".