P.329: Telemicine use in kidney care
Notice bibliographique
Résumé
Kidney disease, encompassing both acute and chronic forms, has emerged as a significant contributor to mortality in the 21st century (Kovesdy, 2022). With over 800 million individuals affected worldwide, the prevalence of chronic kidney disease (CKD) has reached alarming levels, impacting more than 1 in 7 U.S. adults alone (Jager, 2019). Despite its widespread prevalence, a considerable number of individuals remain unaware of their condition, leading to disparities in care and outcomes (Centers for Disease Control and Prevention, 2021). Moreover, racial disparities persist, with Black individuals disproportionately affected by end-stage kidney disease (ESKD) in the United States (United States Renal Data System, 2022). The economic burden of kidney disease is substantial, with Medicare spending for beneficiaries with CKD surpassing $75 billion in 2020 (United States Renal Data System, 2022). This financial strain is particularly concerning in lower-income countries where there is limited or no health coverage for kidney disease treatment. In Africa, notably in Ghana, kidney disease occurrence rates are notably high, often linked with conditions such as glomerulonephritis, diabetes mellitus, and hypertension (Amoako et al., 2014). Despite the pressing need for kidney care, marginalized communities encounter obstacles in accessing care, underscoring the importance of exploring innovative solutions such as telemedicine (Lambooy et al., 2021). The literature review underscores the potential of telemedicine in reducing healthcare costs, minimizing infections, and expediting pretransplant evaluations. Recent research indicates that telehealth significantly improves the efficiency of initial waitlist evaluations for kidney transplantation, leading to better prognoses (Ammary FA et al. 2021). However, challenges in establishing suitable locations for telehealth video conferencing, particularly in the USA due to stringent guidelines, remain (Conception et al. 2020). Notably, there is currently no telemedicine guideline in Ghana. Despite associated costs with telehealth tools and personnel training, they are considered moderate and reliable, with specialized training deemed crucial for effective utilization (Forbes RC et al., 2018). Integration of telehealth in kidney transplant care has demonstrated enhanced efficiency and cost-effectiveness, particularly in initial waitlist evaluations(Forbes RC et al., 2018). Telemedicine holds promise in enhancing access to kidney care in rural areas, with outcomes comparable to standard care. Therefore, conducting a study in Ghana to investigate the feasibility of telemedicine in kidney care is essential for improving access and reducing healthcare costs. Additionally, this research may shed light on the need for increased efforts to develop telemedicine guidelines and provide specialized training to healthcare professionals, particularly in resource-limited settings like Ghana. Vanessa E Silva.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».