Reasons for reoperation after epilepsy surgery: a review based on a complex clinical case with three operations
Bibliographic record
Abstract
José F Téllez-Zenteno1, Farzad Moien-Afshari1, Lizbeth Hernández-Ronquillo1, Robert Griebel2, Venkat Sadanand21Department of Medicine, Division of Neurology, University of Saskatchewan, Saskatoon, Canada; 2Department of Surgery, Division of Neurosurgery, University of Saskatchewan, Saskatoon, CanadaAbstract: The results of surgical treatment of epileptic seizures have gradually improved in the past decade, approaching 60% to 90% seizure-free outcome in temporal lobe epilepsy and 45% to 66% in extratemporal lobe epilepsy. Unfortunately some patients continue with seizures after epilepsy surgery and the studies have shown that approximately the 3% to 15% of patients with a previous failed surgical procedure are reoperated. Selected patients may be candidates for further surgery, potentially leading to a significant decrease in the frequency and severity of seizures. In patients with intractable partial epilepsy there are many possible factors, alone or in combination, that could be related to the failure of resection. Some of the factors could be genetic or acquired predisposition to epileptogenicity. In this article we report a case with intractable epilepsy that required three interventions to render seizure free. We analyzed our specific case in the light of previous reports on reoperation and enumerate the potential reasons or reoperation that could apply to all patients with failure of an initial procedure.Keywords: intractable epilepsy, reoperation, epilepsy surgery, surgical failure
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".