Communication Breakdown? The Perils and Opportunities of ASM Self-Discontinuation
Bibliographic record
Abstract
Risk Factors and Consequences of Self-Discontinuation of Treatment by Patients With Newly Diagnosed EpilepsySharma S, Chen Z, Rychkova M, Dunne J, Lee J, Lawn N, Kwan P. Epilepsy Behav. 2022;131(Pt A):108664. doi:10.1016/j.yebeh.2022.108664Objective: Patients with epilepsy not uncommonly self-discontinue treatment with antiseizure medications (ASM). The rate, reasons for this, and consequences have not been well studied. Methods: We analyzed self-discontinuation of ASM treatment in patients with recently diagnosed epilepsy via review of clinic letters and hospital correspondence in a prospective cohort of first seizure patients. Results: We studied 489 patients with newly diagnosed and treated epilepsy (median age 41, range 14-88, 62% male), followed up for a median duration of 3.0 years (interquartile range [IQR]: 1.2-6.0). Seventy-eight (16.0%) self-discontinued ASM therapy after a median treatment duration of 1.4 years (IQR: 0.4-2.9), and after a median duration of seizure freedom of 11.8 months (IQR: 4.6-31.8). Patients commonly self-discontinued treatment due to adverse effects (41%), perception that treatment was no longer required (35%), and planned or current pregnancy (12%). Patients who self-discontinued were less likely to have epileptogenic lesions on neuroimaging (hazard ratio [HR] = 0.44, 95% confidence interval [CI]: 0.23-0.83), presentation with seizure clusters (HR = 0.32, 95% CI: 0.14-0.69) and presentation with tonic-clonic seizures (HR = 0.36, 95% CI: 0.19-0.70). Patients with shorter interval since the last seizure (HR = 0.76, 95% CI: 0.66-0.86) were more likely to self-discontinue treatment. Sleep deprivation prior to seizures before diagnosis (HR = 1.80, 95% CI: 1.05-3.09) and significant alcohol or illicit drug use (HR = 2.35, 95% CI: 1.20-4.59) were also associated with higher rates of discontinuation. After discontinuation, 51 patients (65%) experienced seizure recurrence, and 43 (84%) restarted treatment. Twenty-two patients (28%) experienced a seizure-related injury after treatment discontinuation. Significance: Self-initiated discontinuation of ASM treatment was not uncommon in patients with newly treated epilepsy. Reasons for discontinuation highlight areas for improved discussion with patients, including the chronicity of epilepsy and management strategies for current or potential adverse effects.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.041 | 0.038 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".