ARE CHILDREN WITH MEDICAL COMPLEXITY (CMC) MEETING TRANSITION-RELATED MILESTONES BEFORE ENTERING THE ADULT HEALTHCARE SYSTEM? A CROSS-SECTIONAL CHART REVIEW
Bibliographic record
Abstract
Abstract BACKGROUND Medical and technological advances have resulted in a growing cohort of children with medical complexity (CMC) who are reaching adulthood. These children are diagnostically heterogenous and require frequent hospitalizations, intensive community services, and medical technology at home to maintain their health. Transitioning CMC to the adult healthcare system has proven challenging for patients and their caregivers, who are comfortable and empowered in the paediatric setting. This multi-faceted process requires the expertise of an interdisciplinary team and one transition can take up to 100 hours of coordination. Although each patient is diagnostically unique, common requirements for transition arise. A standardized approach to transition would empower caregivers to be better advocates for CMC in the adult healthcare system. In order to implement an approach, a baseline understanding of this growing population is required. OBJECTIVES 1. To obtain demographic, medical, and transition-related data for CMC approaching transition to assess whether current transition practices are aligned with critical transition milestones and events, as detailed by key informants 2. To identify optimal targets for intervention in the transition process and produce a standardized toolkit to address gaps DESIGN/METHODS This was a cross-sectional chart review study. It was conducted at one of the largest Canadian complex care programs, which follows 400+ children. Prior to chart review, critical transition-related parameters and milestones were ascertained from interviews with key informants, such as transition coordination experts, the medical lead of the complex care program, family physicians, social workers, and nurse practitioners, who are the most involved with transitioning patients clinically. This previously siloed knowledge was centralized and converted into a standardized clinical toolkit including an age-stratified checklist and caregiver handouts. Having determined key transition parameters, 51 CMC were identified between the ages of 14 and 17 (inclusive) and were included in the study. Demographic, medical, and transition-related data was collected from care plans and clinical notes. Data was subsequently stratified by age and care location. Descriptive statistics were generated for each key transition parameter. RESULTS This cohort of 51 CMC (51% male, 49% female) displayed steady growth between years. On average, these CMC had 9 diagnoses, 6 subspecialists, actively used 8 medications, and were dependent on one home-technology. Only 50% of patients had discussed transition-related topics and 76% of patients did not have a designated transition lead. Although discussion of transition increased with age, many crucial transition-related topics, both in medical and social categories, were not routinely addressed. From a medical perspective, over 40% of patients had not found a family physician, who serve as the primary medical care coordinators in the adult healthcare system. In the final year before transition, less than 50% of patients had been referred to new adult subspecialists and of these patients, half had not met these new providers. Adult social support programs, which provide critical funding for medications, respite, and home care, represent the other major component of transition. At age 17, 50% of patients had not applied for any provincial disability and respite funding, which can take up to 2 years to receive approval for. CONCLUSION This study has identified areas for improvement in medical and social aspects of transition-related planning that may benefit from a standardized clinical toolkit. These are highly complex patients who are not meeting transition-related milestones before turning 18, and are entering the adult healthcare system unprepared. Without approved funding and established medical follow-up, patients and caregivers suffer. Due to the highly heterogenous nature of this patient population, a standardized approach to transition, in tandem with further research and education, is needed.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".