Anti-Epileptic Drugs in Geriatric Neurosurgery: A Review of 669 Neurosurgical Cases
Bibliographic record
Abstract
Background and Purpose: Patients over the age of 75 are more likely to develop epilepsy than children under the age of 10. Patients of all ages are prescribed anti-epileptic drugs; however, those over the age of 65 are the most typically prescribed group.Methods: This is a retrospective study of geriatric cases admitted to the Neurosurgery Department in Khoula Hospital from January 1, 2016 to December 31, 2019. A medical records of 669 patients were identified. Patients' demographics, risk factors, usage of anti-epileptic drug (AED), type of tumor, tumor location, neuro-vital signs diagnosis, Glasgow coma scale on arrival, treatment types, and length of stay (LOS) were recorded.Results: The prevalence of AEDs use was 19%. Patients with traumatic brain injury (TBI) were found to have a higher rate of using AEDs (32.1%) followed by patients with oncological and vascular pathologies, respectively (30.1% and 21.6%). There was a significant relationship between the utilization of AEDs among different neurological diseases investigated (p<0.05). Patients who received surgical interventions were using AEDs much more than patients with conservative management (p=0.001). There was a significant difference in the LOS and the usage of AEDs. Added to that, the results signify a relationship between the intensive care unit (ICU) admission and the utilization of AEDs in which the majority of the patients who were not on AEDs were not admitted to the ICU (p<0.05). Phenytoin was the most commonly used AED among different neurosurgical pathologies in the present study (n=110).Conclusions: AEDs are used as prophylaxis to prevent seizures before most neurosurgical procedures and were commonly prescribed in TBI patients. Phenytoin was found to be the commonest AEDs utilized among the different neurosurgical categories followed by levetiracetam.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".