Morbidity in epilepsy surgery: an experience based on 2449 epilepsy surgery procedures from a single institution
Bibliographic record
Abstract
OBJECT: In this paper the authors aimed to provide information related to major and minor surgical and neurological complications encountered following stereoelectroencephalography and epilepsy surgery.Methods The authors performed a retrospective review of 491 and 1905 patients who underwent intracranial electrode implantation and epilepsy surgery, respectively, between 1976 and 2006 at the Montreal Neurological Institute. All intracranial electrode implantations and surgical procedures were performed by 1 surgeon (A.O.). RESULTS: A total of 6415 electrode implantations and 2449 surgical procedures were done. There were no deaths related to either procedure. There were no major complications after intracranial electrode implantation, and the risks of infection and intracranial hematoma were found to be 1.8 and 0.8%, respectively. The number of electrodes per lobe (p = 0.05) and number of lobes covered (p = 0.04) were significant risk factors for hematoma and infection. Regarding epilepsy surgery, there were no major surgical complications, and the overall minor complication rate was 2.9%. Infection was the most common complication (1.0%), followed by intracranial hematoma (0.7%). Significant risk factors associated with hematomas and infections were the number of reoperations (p = 0.001) and older patient age (p = 0.03). Minor and major neurological complication rates were 2.7 and 0.5%, respectively, and the rate of overall neurological morbidity was 3.3%. Hemiparesis was the most frequent neurological complication (1.5%). CONCLUSIONS: Based on the authors' experience, intracranial electrode implantation is an effective method with an extremely low morbidity rate. Moreover, epilepsy surgery is safe, especially in experienced hands.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".