Utilization of transition care management plans to facilitate transition of adolescents with epilepsy into the adult healthcare system
Bibliographic record
Abstract
Background: For adolescents with epilepsy, there is often a poor system in place to meet their individualized transition needs. Our objectives were 1) to develop epilepsy-specific transition care management plans (TCMPs) to ensure access, and attachment to adult healthcare providers, and 2) to identify strategies for providing support during the transition period, including through the development of physician and patient (or caregiver) navigated web-based tools, resources and recommendations for health system improvements. Methods: Physicians and nurses with expertise in areas including adult and pediatric epilepsy, family medicine, psychiatry, and varied allied health professionals were engaged to generate epilepsy-related TCMPs. Results: Through an iterative process spanning the course of over a year, TCMPs were developed to cover areas including: treatment responsive and resistant epilepsy, ketogenic diet, epilepsy surgery, women’s issues, mental health, and psychosocial aspects of epilepsy. The TCMPs referenced established guidelines and best practices in the literature wherever possible. Caregiver roles and responsibilities were outlined, remaining cognoscent of available provincial resources. Conclusions: Epilepsy specific TCMPs can be developed through a collaborative approach between pediatric and adult healthcare providers, easing the patient experience, creating educated accountability, and providing a forum to identify and address gaps of care in 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".