Comparing kidney transplant outcomes; caveats and lessons
Notice bibliographique
Résumé
The number of people with end-stage renal disease (ESRD) is growing not only in the USA but worldwide secondary to the global epidemic of diabetes and other causes of kidney disease. Dialysis costs are expensive and access to this care is difficult in developing countries. The majority of patients starting dialysis in low-income countries die and or stop treatment within the first 3 months secondary to financial restraints [1]. Kidney transplantation is the treatment of choice for patients with ESRD and improves long-term survival compared with dialysis [2]. As first shown by Wolfe et al .[ 2], kidney transplantation increases life expectancy as well as quality of life. Later studies have shown that this increase in survival is tied in with the quality of the organ and renal function obtained [3, 4]. However, mortality after transplantation has great variability and is influenced by many known factors (i.e. diabetes), but also unknown factors that contribute to the low R 2 of most models and thus provides an incomplete understanding of the reasons behind this variability. Undoubtedly, an important component of long-term survival is adequate care delivery to kidney transplant recipients. It is likely that better and more frequent specialist care would potentially preserve renal function and also help mitigate mortality from hypertension, diabetes and atherosclerotic disease. In addition, earlier infection and malignancy detection could decrease mortality from these very common causes of death in this population. It is possible that the long-term outcomes of kidney transplant in the USA are hampered by financial barriers leading to non-adherence and lack of access to specialists in the field of transplant. In the USA, Medicare beneficiaries lose their coverage for their immunosuppressive drugs 3 years after transplant and care providers receive low reimbursements for these patients. These factors undoubtedly contribute far greater than in other countries that have nationalized healthcare or provide medication coverage for all constituents including Spain [5]. Ojo et al. evaluated the rates of graft failure and death among USA and Spanish kidney transplant recipients. The authors showed a significant difference among the 10-year graft and patient survival, which was significantly better in the Spanish population. The USA had a greater fraction of transplants going to recipients 70 years of age or older compared with Spain. The two countries had similar rates of glomerular disease and polycystic kidney disease as the primary cause of ESRD. The differences, including recipient age at transplant and a higher number of patients with diabetes as a cause of ESRD, could be strong predictors of patient and allograft survival. Diabetes and hypertension are associated with multiple comorbidities including cardiovascular disease which is the leading cause of death for renal transplant patients. However, it is important to note that the US registry data
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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 | 0,272 | 0,574 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,010 |
| Bibliométrie | 0,006 | 0,011 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,007 | 0,013 |
| Science ouverte | 0,015 | 0,010 |
| Intégrité de la recherche | 0,005 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,002 |
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 ».