Bibliographic record
Abstract
Letters21 August 2018Mid- and Long-Term Health Risks in Living Kidney DonorsGeir Mjoen, MD, PhD and Hallvard Holdaas, MD, PhDGeir Mjoen, MD, PhDOslo University Hospital, Oslo, Norway (G.M., H.H.)Search for more papers by this author and Hallvard Holdaas, MD, PhDOslo University Hospital, Oslo, Norway (G.M., H.H.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L18-0340 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:O'Keeffe and colleagues (1) performed several meta-analyses of important outcomes after kidney donation. Among these was an analysis of 4 studies on all-cause mortality among kidney donors, including a Canadian study by Garg and associates (2) that had a median follow-up of 6.5 years, a U.S. study by Segev and coworkers (3) that had a follow-up of 6.3 years, and a Norwegian study from our group (4) that had a median follow-up of 14 years. These 3 studies showed different results.Garg and Segev and their respective colleagues found significantly decreased mortality among kidney donors; our study ...References1. O'Keeffe LM, Ramond A, Oliver-Williams C, Willeit P, Paige E, Trotter P, et al. Mid- and long-term health risks in living kidney donors: a systematic review and meta-analysis. Ann Intern Med. 2018;168:276-84. [PMID: 29379948]. doi:10.7326/M17-1235 LinkGoogle Scholar2. Garg AX, Meirambayeva A, Huang A, Kim J, Prasad GV, Knoll G, et al; Donor Nephrectomy Outcomes Research Network. Cardiovascular disease in kidney donors: matched cohort study. BMJ. 2012;344:e1203. [PMID: 22381674] doi:10.1136/bmj.e1203 CrossrefMedlineGoogle Scholar3. Segev DL, Muzaale AD, Caffo BS, Mehta SH, Singer AL, Taranto SE, et al. Perioperative mortality and long-term survival following live kidney donation. JAMA. 2010;303:959-66. [PMID: 20215610] doi:10.1001/jama.2010.237 CrossrefMedlineGoogle Scholar4. Mjøen G, Hallan S, Hartmann A, Foss A, Midtvedt K, Øyen O, et al. Long-term risks for kidney donors. Kidney Int. 2014;86:162-7. [PMID: 24284516] doi:10.1038/ki.2013.460 CrossrefMedlineGoogle Scholar5. Berger JC, Muzaale AD, James N, Hoque M, Wang JM, Montgomery RA, et al. Living kidney donors ages 70 and older: recipient and donor outcomes. Clin J Am Soc Nephrol. 2011;6:2887-93. [PMID: 22034505] doi:10.2215/CJN.04160511 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Oslo University Hospital, Oslo, Norway (G.M., H.H.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L18-0340. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoMid- and Long-Term Health Risks in Living Kidney Donors Linda M. O'Keeffe , Anna Ramond , Clare Oliver-Williams , Peter Willeit , Ellie Paige , Patrick Trotter , Jonathan Evans , Jonas Wadström , Michael Nicholson , Dave Collett , and Emanuele Di Angelantonio Mid- and Long-Term Health Risks in Living Kidney Donors Linda M. O'Keeffe , Anna Ramond , and Emanuele Di Angelantonio Metrics Cited byWhat happens to the live donor in the years following donation? 21 August 2018Volume 169, Issue 4Page: 265KeywordsChronic kidney diseaseCohort studiesConflicts of interestDisclosureKidneysMortalityRenal analysis ePublished: 21 August 2018 Issue Published: 21 August 2018 Copyright & PermissionsCopyright © 2018 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".