Health-related Quality of Life in Children with Drug Resistant Epilepsy: A Focus on Risk Factors, Measurement, and Surgical Outcomes
Bibliographic record
Abstract
Health-related quality of life (HRQOL) is recognized as the most important outcome of any chronic health condition. Despite growing empirical interest in HRQOL outcomes, important knowledge gaps undermine the burgeoning literature, compromising the translation of findings to clinical care for children with drug resistant epilepsy. In response, this dissertation presents a comprehensive investigation of HRQOL in children with drug resistant epilepsy through three integrated objectives: (1) fine-tune the existing arsenal of HRQOL measures in pediatric epilepsy; (2) identify risk factors for HRQOL in children with drug resistant epilepsy; and (3) examine the influence of low intellectual ability on post-surgical change in HRQOL. Studies 1 and 2 examined HRQOL assessment in children with drug resistant epilepsy across research and clinical settings. Study 1 extended the validity of the Quality of Life in Childhood Epilepsy Questionnaire (QOLCE-55) to children with drug resistant epilepsy, including confirmation of the higher order factor structure; the findings contribute to the robust psychometric profile of the QOLCE-55 as a reliable and valid measure. Study 2 demonstrated promising evidence regarding the psychometric properties of a global measure of quality of life (QOL), a potentially useful tool that can be used by clinicians to evaluate the impact of treatment outcomes in children with drug resistant epilepsy. Study 3 investigated correlates of HRQOL in children with drug resistant epilepsy, finding that lower child IQ, fewer resources available to aid families and caregiver unemployment were uniquely associated with diminished HRQOL. These results highlight the dominant effect of psychosocial factors on child HRQOL, relative to epilepsy-related variables. Lastly, Study 4 assessed change in HRQOL after epilepsy surgery, with a focus on the role of intellectual ability. Our results suggest that children with low intellectual ability can expect to achieve similar post-operative improvements in HRQOL compared to those with normal intelligence. In sum, the four studies included in this dissertation address and overcome many gaps in the literature on pediatric epilepsy and HRQOL. This body of work provides necessary and novel evidence with the potential to improve prognostication, inform presurgical counselling, and identify targets for therapeutic interventions in children with drug resistant 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".