Epilepsy Surgery Series: A Study of 502 Consecutive Patients from a Developing Country
Bibliographic record
Abstract
Purpose. To review the postoperative seizure outcomes of patients that underwent surgery for epilepsy at King Faisal Specialist Hospital & Research Centre (KFSHRC). Methods. A descriptive retrospective study for 502 patients operated on for medically intractable epilepsy between 1998 and 2012. The surgical outcome was measured using the ILAE criteria. Results. The epilepsy surgery outcome for temporal lobe epilepsy surgery (ILAE classes 1, 2, and 3) at 12, 36, and 60 months is 79.6%, 74.2%, and 67%, respectively. The favorable 12- and 36-month outcomes for frontal lobe epilepsy surgery are 62% and 52%, respectively. For both parietal and occipital epilepsy lobe surgeries the 12- and 36-month outcomes are 67%. For multilobar epilepsy surgery, the 12- and 36-month outcomes are 65% and 50%, respectively. The 12- and 36-month outcomes for functional hemispherectomy epilepsy surgery are 64.2% and 63%, respectively. According to histopathology diagnosis, mesiotemporal sclerosis (MTS) and benign CNS tumors had the best favorable outcome after surgery at 1 year (77.27% and 84.3%, resp.,) and 3 years (76% and 75%, resp.,). The least favorable seizure-free outcome after 3 years occurred in cases with dual pathology (66.6%). Thirty-four epilepsy patients with normal magnetic resonance imaging (MRI) brain scans were surgically treated. The first- and third-year epilepsy surgery outcome of 17 temporal lobe surgeries were (53%) and (47%) seizure-free, respectively. The first- and third-year epilepsy surgery outcomes of 15 extratemporal epilepsy surgeries were (47%) and (33%) seizure-free. Conclusion. The best outcomes are achieved with temporal epilepsy surgery, mesial temporal sclerosis, and benign CNS tumor. The worst outcomes are from multilobar surgery, dual pathology, and normal MRI.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".