Trajectories of Health and Well-being in Children with Epilepsy: Hypotheses and Methodology of a Canadian Longitudinal Study
Bibliographic record
Abstract
Introduction Our study quantifies direct, mediating and moderating influences of various epilepsy, co-morbid, child, and family variables on children’s health and well-being over the early life-course. We will present hypotheses and methodology of this prospective longitudinal study based on a conceptual framework for understanding health outcomes. Methods The population is children with epilepsy 8-14 years old and their caregivers from across Canada. Children, caregivers, and health professionals complete 17 measures at five visits over 28 months. The measures are all based on content, the source of the items, psychometric properties, and provision for child self-report. Our cross-sectional and longitudinal design includes a relational model for structural equation modeling of specific biomedical and psychosocial variables with hierarchical direction of influence. We use hierarchical linear modeling to measure change over time. Results Demographics: among 506 families: mean child age 11.4-years; epilepsy onset 6.2 years; epilepsy duration 5.2 years; and mean IQ 99.4. Characteristics: 71% take a single medication; 46% have experienced medication failure; 29% have been seizure free, 31% had low and 37% high seizure severity over the previous year. Discussion Discussion of these perspectives will help researchers consider their methodology and encourage longitudinal studies. Furthermore, our experience may help clinicians identify what to look for when evaluating outcomes research. We believe that the next generation of research to understand life-course effects on the lives of children and youth with chronic conditions and their families must occur over real time.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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".