Use of Antiepileptic Drugs in Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
INTRODUCTION: Controversy exists in antiepileptic drug (AED) prophylaxis prescribing in patients with aneurysmal subarachnoid hemorrhage (SAH). We undertook the Use of Antiepileptic Drugs in Aneurysmal Subarachnoid Hemorrhage (ALIBI) study to identify factors associated with prescribing practices. METHODS: A retrospective chart review of all consecutive patients requiring Level 1 care with aneurysmal SAH admitted between 2012 and 2014 to the intensive care unit at Toronto Western Hospital, Ontario, Canada, was conducted. Data were collected on clinical and imaging characteristics. Primary and secondary outcomes were AED prophylaxis and clinical seizure activity during hospitalization. Data were compared using chi-square or Mann-Whitney U-tests. Those variables found to be significant, or trending toward significance, on univariate analysis were fitted to multivariate regression. RESULTS: Sixty-eight patients were included. Mean age was 62 ± 12.2, and 42.6% of patients were male. Of these, 21 patients (30.9%) received AED prophylactically, while 18 (26.5%) had reported seizures at some point during hospitalization. Female gender and presence of midline shift (MLS) were significantly associated or approached significance with AED prophylaxis in univariate analysis (p = 0.036 and p = 0.062, respectively). In multivariate analysis, only MLS was an independent predictor (odds ratio 5.09, p = 0.04). CONCLUSION: The presence of MLS was an independent predictor of seizure activity in patients with aneurysmal SAH. AED prophylaxis prescribing patterns seemed arbitrary and was not informed by identifiable clinical factors or true risk factors for seizure. A current lack of evidence guiding AED prescribing practice highlights the need for larger studies in this patient population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".