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Enregistrement W1579407579 · doi:10.1111/ajt.13015

Selecting Appropriate Controls for Kidney Donors—Reply

2014· letter· en· W1579407579 sur OpenAlexaffabout
Peter P. Reese, Roy D. Bloom, Harold I. Feldman, Amit X. Garg, Adam Mussell, Justine Shults, Jeffrey H. Silber

Notice bibliographique

RevueAmerican Journal of Transplantation · 2014
Typeletter
Langueen
DomaineMedicine
ThématiqueOrgan Donation and Transplantation
Établissements canadiensWestern University
Organismes subventionnairesAmerican Society of Transplantation
Mots-clésMedicineScopusContext (archaeology)PopulationKidney transplantDiseaseKidney diseaseFamily medicineGerontologyKidney transplantationInternal medicineMEDLINEKidneyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

To the Editor: It is not possible to randomize an individual to become a living kidney donor. Therefore, we agree with Mjøen and Holdaas that it is necessary to examine the comparability of the baseline characteristics of the donor and nondonor groups in any study of living donor outcomes (1.Mjøen G Holdaas H. Selecting appropriate controls for kidney donors.Am J Transplant. 2015; 15: 286Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar). We stand by the conclusion of our manuscript, which reads: “In the context of careful medical evaluation and selection, older donors should expect similar medium-term survival and risk of CVD compared to healthy members of the general population” (2.Reese PP Bloom RD Feldman HI Mortality and cardiovascular disease among older live kidney donors.Am J Transplant. 2014; 14 (et al): 1853-1861Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar), p. 1859. Mjøen and Holdaas (1.Mjøen G Holdaas H. Selecting appropriate controls for kidney donors.Am J Transplant. 2015; 15: 286Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar) note that our methods included restriction of the comparison group to nondonors who, in serial interviews with the Health and Retirement Study (HRS), denied a range of relevant health conditions such as diabetes and cardiovascular disease. The HRS participants in the comparison group also needed to rate their overall health as “good,” “very good,” or “excellent.” These were the strategies used to create a nondonor group with a low prevalence of serious medical problems that would serve as a useful benchmark when interpreting outcomes among older live kidney donors. However, we did not have serological values, medical records or abdominal imaging to further characterize the health of the nondonors. Mjøen and Holdaas recapitulated this limitation that we acknowledged in the manuscript, namely the potential for residual confounding by unrecognized medical problems in the nondonor group (2.Reese PP Bloom RD Feldman HI Mortality and cardiovascular disease among older live kidney donors.Am J Transplant. 2014; 14 (et al): 1853-1861Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar). Mjøen and Holdaas (1.Mjøen G Holdaas H. Selecting appropriate controls for kidney donors.Am J Transplant. 2015; 15: 286Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar) also draw attention to the separation of the donor and nondonor survival curves in our Figure 2. Yet, that separation was not significant (p = 0.21) in the primary analysis (donors ≥55 years). A substantive conclusion about this study’s validity should not be based on this small and statistically insignificant difference in survival, which may only reflect sampling noise. In Table 1, we present a summary of donor comparison groups from recent studies that reported mortality. The table includes an important study by Mjøen et al that also relied in part on data from interviews to generate a healthy comparison group of nondonors (3.Mjøen G Hallan S Hartmann A Long-term risks for kidney donors.Kidney Int. 2014; 86 (et al): 162-167Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar). The table examines whether individuals were excluded from the nondonor groups because of medical abnormalities for which live donors are screened (4.Organ Procurement and Transplantation Network. Policy 172. Living Donation. Available at: http://optn.transplant.hrsa.gov/PoliciesandBylaws2/policies/pdfs/policy_172.pdf. Accessed August 5, 2014.Google Scholar). In each case, the approach taken to assemble the nondonor group has limitations. We hope that future studies will generate new knowledge about long-term donor outcomes using comparison groups that, while likely still imperfect, represent improvements over existing work.Table 1Nondonor groups used as comparators in recent studies of outcomes after live kidney donationData sourceHealth and Retirement Study (Reese et al) (2.Reese PP Bloom RD Feldman HI Mortality and cardiovascular disease among older live kidney donors.Am J Transplant. 2014; 14 (et al): 1853-1861Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar)National Health and Nutrition Examination Survey (Muzaale et al) (5.Muzaale AD Massie AB Wang MC Risk of end-stage renal disease following live kidney donation.JAMA. 2014; 311 (et al): 579-586Crossref PubMed Scopus (668) Google Scholar)Health Study of Nord-Trondelag (Mjøen et al) (3.Mjøen G Hallan S Hartmann A Long-term risks for kidney donors.Kidney Int. 2014; 86 (et al): 162-167Abstract Full Text Full Text PDF PubMed Scopus (547) Google Scholar)National Center for Health Statistics Life Tables (Ibrahim et al) (6.Ibrahim HN Foley R Tan L Long-term consequences of kidney donation.N Engl J Med. 2009; 360 (et al): 459-469Crossref PubMed Scopus (822) Google Scholar)Healthy residents of Ontario, Canada with health system records (Garg et al) (7.Garg AX Meirambayeva A Huang A Cardiovascular disease in kidney donors: Matched cohort study.BMJ. 2012; 344 (et al): e1203Crossref PubMed Scopus (154) Google Scholar)Type of baseline data on nondonorsInterviewsInterviews and limited clinical measurementsInterviews and limited clinical measurementsN/AUniversal healthcare administrative databasesNondonors with this condition eliminated from comparator group1Some additional characteristics used for restriction or matching are not listed in this table.HypertensionYesYesYesNoYesDiabetesYesYesYesNoYesCardiovascular diseaseYesYesYesNoYesKidney diseaseNoYesNoNoYesPulmonary diseaseYesYesNoNoYesLiver diseaseNoNoNoNoYesPsychiatric and neurologic disordersYesNoNoNoNoMalignancyYesYesNoNoYesElevated BMIYes (≥40 kg/m2When available for donors.)Yes (>30 kg/m2When available for donors.)Yes (>30 kg/m2When available for donors.)NoNoLow self-rated healthYesNoYesNoNoPoor functional statusNoYesNoNoNoAbnormalities on kidney imagingNoNoNoNoNoCharacteristic used for matching1Some additional characteristics used for restriction or matching are not listed in this table.DemographicsYesYesYesNoYesBMIYes1Some additional characteristics used for restriction or matching are not listed in this table.Yes1Some additional characteristics used for restriction or matching are not listed in this table.YesNoNoBlood pressureNoYes1Some additional characteristics used for restriction or matching are not listed in this table.YesNoNoNeighborhood IncomeYesNo (but, did match on education)NoNoYesYear of cohort entry (donation date for donors)YesNoNoNoYesNumber of nondonors3 3689 36432 621N/A20 280Number of donors3 36896 2171 9013 6982 0281 Some additional characteristics used for restriction or matching are not listed in this table.2 When available for donors. Open table in a new tab This study was funded by the American Society of Transplantation. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,030
score de la tête « metaresearch » (Gemma)0,160
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,159

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0300,160
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,005
Communication savante0,0040,007
Science ouverte0,0060,003
Intégrité de la recherche0,0270,042
Charge utile insuffisante (le modèle a refusé de juger)0,0070,004

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,265
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2014
Routes d'admission2
Résumé présentoui

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