Adherence to levetiracetam for management of epilepsy: Assessment with electronic monitors
Bibliographic record
Abstract
INTRODUCTION: Anti-seizure medications are used to manage epilepsy and require long-term adherence to maintain therapeutic drug levels. We assessed adherence to levetiracetam and the use of a digital intervention to improve adherence in patients with epilepsy. METHODS: 30 participants with epilepsy were randomized 1:1 either to a digital email adherence intervention or control group. All patients were provided levetiracetam equipped with electronic monitoring caps to assess patient adherence to medication. Patients were followed for 6 months, with return visits at 1 month, 3 months, and 6 months. RESULTS: Subjects randomized to the control arm (n = 15) took 66% of the prescribed doses compared to the intervention group, who took 65% of prescribed doses (n = 15). Nine participants did not complete the study. Of the twenty-one participants that completed the study, the overall rate of adherence was 72% of prescribed doses taken. Two subjects in the control group and three subjects in the intervention group were adherent every month of the study-taking at least 80% of prescribed doses. Those randomized to the control group took the correct number of doses 44% of days in the study, and those in the intervention group took the correct number of doses 37% of days. DISCUSSION: Poor adherence to levetiracetam is common. An internet-based email survey intervention did not improve adherence to levetiracetam in epilepsy patients. Further advances in adherence are needed to help patients receive the maximum benefit of their medical treatments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".