Predictors of long‐term quality of life after pediatric epilepsy surgery
Bibliographic record
Abstract
OBJECTIVE: This study examined the influence of seizures, antiepileptic drugs (AEDs), intelligence quotient (IQ), and symptoms of depression and anxiety on health-related quality of life (HRQOL) 4-11 years after pediatric epilepsy surgery. METHODS: Participants were 109 patients with childhood-onset intractable epilepsy; 71 had undergone surgery on average 6.9 years before this study. Patients and their parents completed questionnaires assessing HRQOL and internalizing behavior, a measure of depression and anxiety symptoms. RESULTS: Similar rates of recent seizure freedom were found for surgical and nonsurgical patients, although surgical patients had achieved seizure freedom sooner and with fewer AEDs. Few differences were found between surgical and nonsurgical patients. Differences emerged when comparing patients with continued seizures and those who had been seizure-free in the 12 months preceding the study. Almost all HRQOL ratings were enhanced in seizure-free patients. Internalizing behavior (anxiety/depression) mediated the relationship between seizure freedom and better HRQOL, where seizure freedom led to better ratings of anxiety/depression, which in turn led to better ratings of HRQOL. AED use was found to be associated with social functioning, medication effects, and seizure worry. IQ and duration of follow-up were not found to independently influence HRQOL. SIGNIFICANCE: The findings highlight the integral role of depression and anxiety symptoms in determining HRQOL; seizure control seems to play a secondary role. This study expands this relationship to individuals who have a history of intractable childhood epilepsy. The findings highlight the importance of managing depression and anxiety in improving the HRQOL and reducing seizure burden on patients.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".