MétaCan
Menu
Back to cohort
Record W1579407579 · doi:10.1111/ajt.13015

Selecting Appropriate Controls for Kidney Donors—Reply

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

Bibliographic record

VenueAmerican Journal of Transplantation · 2014
Typeletter
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsWestern University
FundersAmerican Society of Transplantation
KeywordsMedicineScopusContext (archaeology)PopulationKidney transplantDiseaseKidney diseaseFamily medicineGerontologyKidney transplantationInternal medicineMEDLINEKidneyEnvironmental health

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.160
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.160
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0040.007
Open science0.0060.003
Research integrity0.0270.042
Insufficient payload (model declined to judge)0.0070.004

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.

Opus teacher head0.012
GPT teacher head0.265
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of TransplantationSame topicOrgan Donation and TransplantationFrench-language works237,207