Disconnective Approach Leads to Superior Seizure Outcome Compared to Other Hemispheric Procedures—A Meta‐Analysis
Bibliographic record
Abstract
Hemispherectomy is the most promising treatment for patients with severe hemispheric intractable epilepsy. Several techniques for this surgical intervention have been established, but the choice of technique is currently mostly dependent on the surgeon’s experience with a specific approach. We aim to demonstrate whether the choice of the surgical technique moderates surgical outcome in patients with severe hemispheric intractable epilepsy, as measured by seizure freedom and the incidence of death after surgery. We extracted 2382 articles from PubMed and Cochrane. Two independent experts selected 555 articles. We performed a meta‐analysis for all studies and a pooled data analysis for studies where information on individual patients was available. None of the retrieved studies was randomized. Disconnective surgery yielded significantly higher rates of seizure freedom (0.83) than resective (0.70, p < 0.001) or combined surgery (0.64, p < 0.001) for patients with at least 1 year follow–up ( N cases = 1165). For death ( N cases = 1197), resective surgery had the highest rate of death within a year (0.07), significantly higher than disconnective surgery (0.012, p = 0.001) and combined surgical techniques (0.006, p < 0.001). The assessed techniques did not systematically differ in rate of acute complications, but in their type, for example, acute neurological complications were most common after disconnective surgery ( p < 0.001), unspecific symptoms after resective surgery ( p < 0.004). Chronic neurological complications were most common after resective surgery ( p < 0.001). Seizure freedom is more likely following disconnective surgery as compared to resective or combined techniques. Disconnective and combined surgical techniques lead to fewer chronic complications and death than resective approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.001 | 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 teacher head, 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".