Risk factors for health‐related quality of life in children with epilepsy: A meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to conduct a meta-analysis of risk factors for health-related quality of life (HRQL) in children with epilepsy; interpret the results in terms of study quality; and, assess the nature and source of heterogeneity of estimates. METHODS: Databases were searched for studies that examined HRQL in pediatric epilepsy. The inclusion criteria were original studies published in English from 1994 through to the end of January 2014; children ≤18 years of age with epilepsy; included a parent- or self-reported measure of HRQL; and, data were presented such that the calculation of a correlation coefficient was possible. Study quality was measured using a modified Quality Index. RESULTS: A total of 12 risk factors from 21 studies were analyzed. The mean Quality Index score was 10.4 (standard deviation [SD] 1.9). Correlations between risk factors and HRQL had a minimum of r = -0.03 and a maximum of r = -0.44. Child sex, age, and age at onset were not significantly associated with HRQL. Duration of epilepsy, seizure type, frequency, and severity, number of antiepileptic drugs, side effects of antiepileptic drugs, presence of a comorbidity, parental anxiety, and family socioeconomic status were significantly associated with HRQL. Informant (child vs. parent), year of publication, and study quality were found to be sources of heterogeneity for certain risk factors. SIGNIFICANCE: Results demonstrated that a variety of clinical and family factors are associated with HRQL in children with epilepsy and have implications for research and practice. Future research should focus on longitudinal studies to identify predictors of HRQL that are amenable to intervention and should evaluate whether changes in these predictors result in more favorable HRQL in children with epilepsy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".