ED-15 * LONG-TERM PROPHYLACTIC ANTIEPILEPTIC DRUGS USE IN PATIENTS WITH GLIOMAS: A 7 YEAR RETROSPECTIVE ANALYSIS IN A TERTIARY CARE CENTER
Bibliographic record
Abstract
BACKGROUND: American Academy of Neurology's 2000 practice parameter do not support routine use of prophylactic antiepileptic drugs (AEDs) in newly diagnosed brain tumors. If used in the perioperative setting, they should be discontinued after the first postoperative week. However, it remains unclear whether such recommendations are followed. OBJECTIVE: To compare our use of long-term prophylactic AEDs in patients with gliomas to published practice guidelines by the AAN. METHODS: A retrospective chart review was performed on 578 glioma cases evaluated in a single tertiary care center from 2006-2013. Data collected included demographic factors, surgical procedure and tumor characteristics. Seizures and AED use were assessed at surgery, 3 months post-operatively and death/last visit/16 months. Long-term prophylactic AED use was defined as continued use of AED at 3 months post-surgery in the absence of seizures. Patients were divided into three groups at surgery: seizure-free with and without prophylactic AEDs, and seizure-patients. Survival, prophylaxis efficacy and factors influencing its use were calculated. RESULTS: Out of 578 patients operated between 2006-2013, 330(57.1%) were seizure-naïve pre-operatively. 205/330(62.1%) received prophylactic AED at surgery. 96/205(46.9%) 95%CI(40.2-54.7) were still on AED 3 months post-surgery (median use = 58 days; 95%CI(31-152)). Rate of long-term prophylaxis use decreased by 13.5% over 6 years (70.3%-2006;56.8%-2012). Dilantin was the preferred agent in 2006(98.2%) with increasing use of Keppra over the years (44.6%-2012). There were no significant differences in age, histology, localisation and resected status between the seizure-free populations with and without prophylaxis. However, seizure-population had more men (p = 0.0068), younger patients (p < 0.0001), lower-grade gliomas (p = 0.0003) and lived longer (p = 0.0012,HR = 0.5423,95%CI(0.3742, 0.7859)) compared to seizure-free populations. The only predictive factor for prophylactic AEDs use was complete resection (p = 0.0069,OR = 2.0292,95%CI(1.2202, 3.4177)). Prevalence of first seizure was similar in both seizure-free populations (p = 0.9104, HR = 0.9627, 95%CI(0.5153, 1.806)). CONCLUSIONS: In our centre, long-term prophylactic AED use is high, deviating from current AAN Guidelines. Corrective measures are warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".