P.047 The importance of assessing mental health in transition-aged adolescents with epilepsy
Bibliographic record
Abstract
Background: When compared to the general population, researchers have reported elevated rates of mental health issues in the pediatric epilepsy population. These issues have been found to be especially problematic around the time of transition from pediatric to adult care. This is significant because depression and/or anxiety have been found to be directly related to worsened seizure outcomes and quality of life. Despite this, no known Canadian pediatric epilepsy centers have integrated mental health assessment into mainstream practice. Methods: To explore the importance of mental health assessments, we looked at the prevalence rates of both depression and anxiety in 91 adolescents with epilepsy aged 14 to 18 (M=16.3, 51 males, 41 females) enrolled into an epilepsy transition clinic. Results: 58.3% of adolescents showed signs of depression (28.6% mild, 21.4% moderate, 6.0% moderately-severe, 2.4% severe), and 51.8% of adolescents showed signs of anxiety (31.8% mild, 10.6% moderate, 9.4% severe). Remarkably, 54.8% of patients presenting with moderate to severe depression and/or anxiety had not been previously identified Conclusions: These results suggest that in order to ensure the best possible outcomes for patients, mental health assessments should be integrated into the standard model of care for transition-aged adolescents with 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".