Examining Shared-Care Models for the Long-Term Management of Stable Kidney Transplant Recipients
Bibliographic record
Abstract
Background Advances in transplantation procedures, immunosuppressive agents, and the management of comorbid conditions have led to better outcomes in kidney transplant recipients (KTR). The subsequent rise in the number of KTR and the duration of post-transplant follow-up care places a strain on the limited resources of specialized transplant centres. Thus, innovative models of long-term care for KTR is needed to alleviate this strain and improve the capacity of these centres. Shared-care practices, integrating community nephrologists with the transplant team, is an approach to improve the efficiency and quality of care provided to stable KTR. A literature review was conducted to investigate existing shared-care models in KTR while a scoping review was conducted to further explore shared care models in chronic disease management. Methods For the primary literature review, Medline and CINAHL were searched with keywords including “kidney transplant”, “long-term”, “community”, and “care”. For the scoping review, Medline was searched with terms including “diabetes”, “heart failure”, “shared-care”, “long-term”, and “care”. We included peer-reviewed articles and abstracts published in English from 1970 to 2016. Two reviewers independently assessed and extracted data from included articles. Results After screening 1053 articles for the primary search, 13 were found eligible and included in the review. The articles from the primary search separated into two major themes: articles explaining shared care protocols for the long-term management of KTR and articles summarizing clinical management strategies after transplantation. Four articles described specific shared-care models with community nephrologists by suggesting a timeline for transfer and follow-up, outlining guidelines for communication, and identifying possible setbacks to implementation. Due to limited information obtained on implementation and evaluation of shared-care models, we expanded our review to consider models used in the management of heart failure and diabetes. Some important factors in the implementation of shared-care for these complex conditions included the role of a specialist nurse to facilitate coordination between centres as well as standardized clinical and referral guidelines. Shared-care practices generally resulted in improvements in information exchange and received positive responses from patients and healthcare staff. Patient health outcomes were found to be at least comparable to (and sometimes better than) less collaborative models. Conclusion While a standardized shared-care model for the management of stable KTR has not yet been established, sharing of patient care responsibilities between the transplant center and community nephrologists optimizes access to post-transplant care, may improve the quality of care, and reduces the burden on transplant centres.
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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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".