Trajectories of health‐related quality of life in children with epilepsy: A cohort study
Bibliographic record
Abstract
PURPOSE: Little is known about subgroups of children with epilepsy who may experience less favorable outcomes over time. The objectives of this study were to document trajectories of health-related quality of life (HRQL) and to identify predictors of the trajectory group in children with new-onset epilepsy. METHODS: Data were obtained from the Health Related Quality of Life in Children with Epilepsy Study, a prospective multisite study of children 4-12 years old with new-onset epilepsy followed for 24 months. Health-related quality of life was measured using the Quality of Life in Childhood Epilepsy questionnaire. Trajectories of HRQL were investigated using latent class trajectory modeling. Multinomial logistic regression was used to identify child, parent, and family predictors of HRQL trajectories. KEY FINDINGS: A total of 374 families responded at baseline and 283 (76%) completed the study. Five HRQL trajectories were observed: low-increasing (4%), moderate-decreasing (12%), moderate-increasing (22%), high-increasing (32%), and high-stable (30%). Many children in the low-increasing, moderate-increasing, high-increasing, and high-stable had clinically meaningful improvements in HRQL: 82%, 47%, 63%, and 44%, respectively. In contrast, the majority of children in the moderate-decreasing group (56%) experienced clinically meaningful declines in their HRQL. Factors predicting trajectories were number of antiepileptic drugs prescribed, presence of comorbid behavior or cognitive problems, parent depression, and family functioning and demands. SIGNIFICANCE: Results suggested that children with epilepsy are not homogenous but rather consist of groups with different trajectories and unique predictors of HRQL. Problems associated with child behavior and cognition were the strongest predictors identified. Given that several risk factors are modifiable, it is important to examine these as potential targets within a family-centered framework to improve HRQL of children with new-onset 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".