Seizure Freedom Reduces Illness Intrusiveness and Improves Quality of Life in Epilepsy
Bibliographic record
Abstract
BACKGROUND: Chronic illnesses are associated with multiple stressors that compromise quality of life (QOL). Implicit in many of these stressors is the concept of illness intrusiveness: the disruption of lifestyles, activities, and interests due to the constraints imposed by chronic disease and its treatment. The purpose of this study was to examine illness intrusiveness and QOL in epilepsy in patients with different levels of seizure control. METHODS: Cross-sectional data were obtained and compared between two groups of patients categorized by presence of seizures: seizure freedom or continued seizures (N = 145). Standard instruments measured the following variables: illness intrusiveness, perceived personal control, subjective well-being, and disease specific QOL. RESULTS: Illness intrusiveness varied inversely and significantly with seizure control. Complete seizure freedom, whether achieved by pharmacological or surgical treatment, was associated with the lowest levels of illness intrusiveness. Seizure freedom was also associated with increased perceived control, positive affect, self-esteem and QOL in epilepsy. CONCLUSIONS: The most robust benefits of decreased illness intrusiveness in epilepsy occur when treatment leads to complete seizure control. Therefore every effort should be made by health care providers to achieve seizure freedom to reduce illness intrusiveness and improve QOL in epilepsy.
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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 | 0.000 | 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) | 0.001 | 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".