B.1 What Transition Skills Should be Targeted in Epilepsy Transition Clinics?
Bibliographic record
Abstract
Background: Transition from pediatric to adult care can be a difficult time for adolescents with epilepsy. This period is often a period of extreme vulnerability and stress. As a result, research has recommended transition clinics to help these adolescents develop needed transition skills. However, the skills that need to be focused on remain unclear. Methods: Baseline transition skills in 113 adolescents with epilepsy, aged 14 to 18 (M= 16.46, male= 56) were analyzed. Results: Analyses showed that older adolescents showed significantly more transition skills than younger adolescents (F(4,108)=5.522, p=000). Although positive, older adolescents only scored, on average, 16.3/28 on the transition questionnaire; suggesting that many skills are still lacking, even at the time of transition. Specifically, although the majority of these older adolescents demonstrated being able to manage their condition independently (e.g., summarizing medical history, taking/knowing medications), these adolescents were less likely to demonstrate skills needed to be advocates for themselves and their health (e.g., asking questions, discussing concerns, speaking to the doctor instead of letting their parents). Conclusions: Results suggest it may be beneficial to restructure adolescent clinic visits; encouraging these patients to attend the initial portion of visits independently to help them feel more comfortable and confident championing for themselves.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".