Predictors for and use of rescue medication in adults with epilepsy: A multicentre cross-sectional study from Germany
Bibliographic record
Abstract
BACKGROUND: Seizure clusters, prolonged seizures, and status epilepticus are life-threatening neurological emergencies leading to irreversible neuronal damage. Benzodiazepines are current evidence-based rescue therapy options; however, recent investigations indicated the prescription of mainly unsuitable benzodiazepines and inappropriate use of rescue medication. OBJECTIVE: To examine current use, satisfaction, and adverse events concerning rescue medication in patients with epilepsy in Germany. PATIENTS AND METHODS: The study was conducted at epilepsy centres in Frankfurt am Main, Greifswald, Marburg, and Münster between 10/2020 and 12/2020. Patients with an epilepsy diagnosis were assessed based on a questionnaire examining a 12-month period. RESULTS: In total, 486 patients (mean age: 40.5, range 18-83, 58.2 % female) participated in this study, of which 125 (25.7 %) reported the use of rescue medication. The most frequently prescribed rescue medications were lorazepam tablets (56.8 %, n = 71 out of 125), buccal midazolam (19.2 %, n = 24), and rectal diazepam (10.4 %, n = 13). Seizures continuing for over several minutes (43.2 %, n = 54), seizure clusters (28.0 %, n = 35), and epileptic auras (28.0 %, n = 35) were named as indications, while 28.0 % (n = 35) stated they administered the rescue medication for every seizure. Of those continuing to have seizures, 46.0 % did not receive rescue medication. On average, rescue medication prescription occurred 7.1 years (SD 12.7, range 0-66) after an epilepsy diagnosis. CONCLUSIONS: Unsuitable oral benzodiazepines remain widely prescribed for epilepsy patients as rescue medication. Patients also reported inappropriate use of medication. A substantial proportion of patients who were not seizure-free did not receive rescue medication prescriptions. Offering each patient at risk for prolonged seizures or clusters of seizures an individual rescue treatment with instructions on using it may decrease mortality and morbidity and increase quality of life. .
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".