Can we do better supporting young adults with cerebral palsy as they navigate adulthood? A review of current and future transitional practices
Bibliographic record
Abstract
OBJECTIVE: Patients with cerebral palsy (CP) face lifelong consequences of their condition, and their healthcare needs evolve as they age. Transitional care for these patients is not universally available and various models have been described. In this article, the authors review the current literature surrounding transitional care for patients with CP, focusing predominantly on the neurosurgical aspects of transitional care, and they describe current approaches adopted by programs in North America. They further describe their own experience developing a transitional care clinic for patients with CP, as well as the integration of this program with a multidisciplinary clinic to address the specific challenges that growing patients face in our region. METHODS: The authors performed a literature review to identify models, barriers, and assessments of effective transitional care for CP patients. They also reviewed the recommendations of various professional societies regarding transitional care practices. They performed qualitative analysis of the relevant literature. RESULTS: Transitional care has been broadly categorized into transitional care clinics with multidisciplinary teams and facilitator-led transitional care. CP patients have to overcome a variety of barriers, including those from within the healthcare system as well as environmental and personal, during the period of their transition. These challenges are all interconnected, and navigation requires healthcare professionals to work closely with patients and their caregivers. Multiple instruments are described to measure successful transition, which is likely a reflection of the unique needs that a patient may require. Current guidelines recommend that neurosurgeons select a suitable model of care based on their own local practice and available services, develop a well-defined transition plan, and identify a primary transition facilitator or care coordinator. CONCLUSIONS: Providing effective transitional care to CP patients remains challenging given the different models of care and the barriers faced by them during the period of transition. In developing a transitional care program for these patients, attention must be given to the resources that are available regionally, with an effort to incorporate the best practices from successful transitional care programs.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".