Trajectories of quality of life 10 years following a diagnosis of epilepsy in childhood
Bibliographic record
Abstract
OBJECTIVE: This study estimated trajectories of health-related quality of life (HRQOL) over a 10-year period among children newly diagnosed with epilepsy. We also modeled the characteristics of children, parents, and families associated with each identified trajectory. METHODS: Data came from the HERQULES (Health-Related Quality of Life in Children With Epilepsy Study), a Canada-wide prospective cohort study of children (aged 4-12 years) with newly diagnosed epilepsy. Parents reported on their children's HRQOL at diagnosis, and at 0.5-, 1-, 2-, 8-, and 10-year follow-ups using the Quality of Life in Childhood Epilepsy Questionnaire-55. Trajectories of HRQOL were identified using latent class growth models. Characteristics of children, parents, and families at the time of diagnosis that were associated with each trajectory were identified using multinomial logistic regression. RESULTS: A total of 367 children were included. Four unique HRQOL trajectories were identified; 11% of the cohort was characterized by low and stable scores, 18% by intermediate and stable scores, 35% by intermediate scores that increased then plateaued, and 43% by high scores that increased then plateaued. Absence of comorbidities, less severe epilepsy, and better family environment (greater satisfaction with family relationships and fewer family demands) at the time of diagnosis were associated with better long-term HRQOL trajectories. Although the analyses used estimates for missing values and accounted for any nonrandom attrition, the proportion of children with poorer HRQOL trajectories may be underestimated. SIGNIFICANCE: Children with new onset epilepsy are heterogenous and follow unique HRQOL trajectories over the long term. Overall, HRQOL improves for the majority in the first 2 years after diagnosis, with these improvements sustained over the long term.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".