A Global Perspective on Transition Models for Pediatric to Adult Cystic Fibrosis Care: What Has Been Made So Far?
Bibliographic record
Abstract
Interest in the transition of care for cystic fibrosis (CF) patients has grown significantly over time, driven by advancements in treatment that have extended life expectancy. As more CF patients survive into adulthood, the need for structured transition strategies has become a priority for healthcare systems worldwide. Transition programs for CF differ globally, reflecting varying resources and healthcare systems. In North America, the US CF Foundation has fostered adult care since the 1990s, with accreditation standards mandating adult programs and structured transition guidelines, exemplified by the CF RISE program for gradual responsibility shifts. Canada integrates US-inspired models, emphasizing national advocacy and outcomes evaluation. In Europe, approaches varies widely; the UK leads with structured programs like the Liverpool model and robust registry support, while France and Germany adopt multidisciplinary methods. In Australia and New Zealand, youth-centered policies prioritize early planning and access via telemedicine. In Asia, where CF is rare, transitions are less formalized, with some progress in countries like Japan and Turkey, though resource gaps and limited data tracking remain significant challenges. Despite varied approaches across countries, common barriers like resource limitations and psychological readiness continue to challenge successful transitions. Highlighting the importance of centralized, well-coordinated transition programs, recent initiatives have focused on the implementation of national and international CF registries to enhance health outcomes and quality of life. This narrative review provides a global perspective on transition strategies developed across various healthcare systems for CF patients, identifying best practices, common challenges, and outcomes related to the continuity of care.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".