Examining Shared-Care Models for the Long-Term Management of Stable Kidney Transplant Recipients
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,014 | 0,015 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,008 | 0,007 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».