B.05 The importance of mental health in improving quality of life in transition-aged patients with epilepsy
Bibliographic record
Abstract
Background: Growing evidence has that a suggested that mental health strongly influences quality of life (QoL) in adolescents with epilepsy. In addition, research has suggested that these mental health issues are associated with increased seizure burden and worsened health outcomes. Despite this, and the elevated rate of mental health issues in this population, seizure control tends to be the dominant or sole concern for treating physicians. Methods: In order to look at potential predictors of QoL in adolescents we looked at seizure related data, demographic variables, and comorbid conditions in 70 adolescents with epilepsy aged 14 to 18 (M= 16.3l; 37 males, 33 females) enrolled into an epilepsy transition clinic. Results: Regression analysis found that mental health remained a significant and independent predictor of QoL even when other significant seizure related variables were accounted for (t(58)= -3.44, p= .001). Furthermore, when looking at the individual subscales of patient QoL (e.g., memory, social support, stigma), mental health was consistently found to be the strongest correlate. Conclusions: These results demonstrate that in order to ensure the best outcomes for transition-aged adolescents with epilepsy, it is important to not only manage and treat seizures, but also to assess and treat mental health issues.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".