An Interdisciplinary Approach to Optimize the Care of Transitioning Adolescents and Young Adults with CKD
Bibliographic record
Abstract
Adolescents and young adults (AYAs) with CKD or end-stage kidney disease (ESKD) have unique medical, dental, psychosocial, neurocognitive, and academic needs and require close interdisciplinary collaboration to optimize their care. The etiology of CKD in AYAs is diverse compared to older adults. With their continuously improved survival, AYAs must start preparation for health-care transition (HCT) from pediatric- to adult-focused health care in the pediatric setting and it must continue at the adult-focused setting, given that their brain maturation and self-management skill acquisition occur until their mid-20s. While the growth and physical maturation of most visible body parts occur before 18 years of age, the prefrontal cortex of the brain, where reasoning, impulse control, and other higher executive functions reside, matures around 25 years of age. The HCT process must be monitored using patient- and caregiver-measuring tools to guide interventions. The HCT process becomes more complex when patients and/or caregivers have a language barrier, different cultural beliefs, or lower literacy levels. In this article, we discuss the unique comorbidities of pediatric-onset CKD/ESKD, provide information for a planned HCT preparation, and suggest interdisciplinary coordination as well as cultural and literacy-appropriate activities to achieve optimal patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".