Bibliographic record
Abstract
INTRODUCTION: One in every four children who suffer epilepsy will continue to seize despite all the current arsenal of anticonvulsant medications. Epilepsy surgery can be an effective treatment for many cases of pharmaco-resistant epilepsy. AIM: To review some of the most recent data regarding indication and results for epilepsy surgery in children. DEVELOPMENT: New analysis of neurophysiological data such as magnetoencephalography, dipole analysis and high frequency oscillations, plus functional neuroimaging and higher resolution MRI, have improved the prompt referral of potential candidates for surgery. An adequate selection of the surgical candidates will be fundamental in obtaining positive outcomes. Children, just like adults, can achieve seizure freedom or more than 90% seizure reduction in 85% of the cases of temporal lobe resections. Extratemporal epilepsy surgery is more challenging, and presents frequently in epilepsy surgery centres specialized in pediatric care. CONCLUSION: It is not mandatory for a patient to have an structural lesion evident on MRI in order to be considered a candidate for epilepsy surgery. In many cases a partial reduction of seizure activity can significantly improve the patient's and family's quality of life, and can have a sinergistic effect with other therapies which were no effective before surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.012 | 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".