Determinants for the Initiation of Anti-seizure Medication in a Canadian Tertiary Emergency Department (P4-1.011)
Bibliographic record
Abstract
Objective: A quantitative study of the characteristics and management of patients presenting with presumed acute seizures at a tertiary care emergency department (ED) during defined pre versus peak COVID-19 pandemic periods. We aim to uncover determinants for initiating anti-seizure medications (ASM) as well as the effects of the pandemic. Background: As continuous EEG is unavailable due to resource scarcity, spot EEGs became the only electro-diagnostic method to evaluate suspected seizures. We observed a heavy reliance on spot EEGs to make clinical decisions regardless of seizure types, and wondered what variables in combination influence a decision to start ASM. Design/Methods: This is a retrospective review of 115 adult patients with a spot EEG in the ED. The independent variables are sex, age, motor semiology, EEG requisition indication, EEG results, and acute symptomatic seizure (ASyS) status. The dependent variable is ASM initiation. We applied Chi-squared, univariate and multivariate logistic regression models to find statistically significant determinants of ASM initiation. Results: There were 63 pre-pandemic and 52 peak-pandemic patients. Demographics of patients selected for EEG did not change between pre and peak-pandemic periods, which allow combining both populations for logistic regressions. The univariate logistic regression models reveal motor semiology (p=0.002), EEG results (p<0.001), and ASyS (p=0.003) were statistically significant determinants of ASM initiation. In a multivariate model, ASyS is no longer statistically significant (p=0.199), whereas EEG results (p<0.001) and motor semiology (p=0.002) remain significant. Conclusions: While our study demonstrates motor symptoms and increasing EEG result severity were significant determinants of ASM initiation, we emphasize that an approach heavily relying on spot EEG results to initiate ASM is problematic, as spot EEGs are known for low sensitivities, and during the period immediately after a presumed seizure event, they do not generally predict or rule out an enduring predisposition to have recurrent seizures. Disclosure: Dr. Young has nothing to disclose. The institution of Dr. Ng has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Eisai Canada. The institution of Dr. Ng has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Paladin Canada. The institution of Dr. Ng has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Eisai Canada. The institution of Dr. Ng has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for UCB Canada. Dr. Ng has received publishing royalties from a publication relating to health care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".