Patient emotions and perceptions of antiepileptic drug changes and titration during treatment for epilepsy
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of antiepileptic drug (AED) change and dose titration on the emotional well-being of patients with epilepsy. METHODS: Members of an online epilepsy community were invited to voluntarily participate in an online survey. The cross-sectional anonymous survey consisted of 31 multiple choice questions balanced in terms of variety and positivity/negativity of emotions concerning participants' most recent AED change. To substantiate survey results, spontaneous comments from epilepsy-related online forums and social media websites that mentioned participants' experiences with AED medication changes (termed passive listening statements) were analyzed and categorized by theme. RESULTS: All 345 survey participants (270 [78.3%] female; 172 [49.9%] were 26-45years old) self-reported an epilepsy/seizure diagnosis and were currently taking seizure medication; 263 (76.2%) were taking ≥2 AEDs and 301 (87.2%) had ≥1 seizure in the previous 18months. All participants reported a medication change within the previous 12months (dose increased [153 participants (44.3%)], medication added [105 (30.4%)], dose decreased [49 (14.2%)], medication removed [38 (11.0%)]). Improving seizure control (247 [71.6%]) and adverse events (109 [31.6%]) were the most common reasons for medication change. Primary emotions most associated (≥10% of participants) with an AED regimen change were (before medication change; during/after medication change) hopefulness (50 [14.5%]; 43 [12.5%]), uncertainty (50 [14.5%]; 69 [20.0%]), and anxiety (35 [10.1%]; 45 [13.0%]), and were largely due to concerns whether the change would work (212/345 [61.4%]; 180/345 [52.2%]). In the text analysis segment aimed at validating the survey, 230 participants' passive listening statements about medication titration were analyzed; additional seizure activity during dose titration (93 [40.4%]), adverse events during titration (71 [30.9%]), higher medication dosages (33 [14.3%]), and drug costs (25 [10.9%]) were the most commonly noted concerns. CONCLUSION: Although the emotional well-being of patients with epilepsy is complex, our study results suggest that participants report their emotional well-being as negatively affected by changes in AED regimen, with most patients reporting uncertainty regarding the outcome of such a change. Future research is warranted to explore approaches to alleviate patient concerns associated with AED medication changes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".