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Record W2909182150 · doi:10.1016/s2213-8587(18)30313-9

Change in albuminuria and subsequent risk of end-stage kidney disease: an individual participant-level consortium meta-analysis of observational studies

2019· review· en· W2909182150 on OpenAlexaff
Josef Coresh, Hiddo J.L. Heerspink, Yingying Sang, Kunihiro Matsushita, Johan Ärnlöv, Brad C. Astor, Corri Black, Nigel J. Brunskill, Juan Jesús Carrero, Harold I. Feldman, Caroline S. Fox, Lesley A. Inker, Areef Ishani, Sadayoshi Ito, Simerjot K Jassal, Tsuneo Konta, Kevan R. Polkinghorne, Solfrid Romundstad, Marit D. Solbu, Nikita Stempniewicz, Bénédicte Stengel, Marcello Tonelli, Mitsumasa Umesawa, Sushrut S. Waikar, Jack F.M. Wetzels, Mark Woodward, Morgan E. Grams, Csaba P. Kövesdy, Andrew S. Levey, Ron T. Gansevoort, Lawrence J. Appel, Tom Greene, Teresa K. Chen, John Chalmers, Hisatomi Arima, Vlado Perkovic, Adeera Levin, Ognjenka Djurdjev, Mila Tang, Joseph V. Nally, Sankar D. Navaneethan, Jesse D. Schold, Misghina Weldegiorgis, William G. Herrington, Margaret Smith, Chun-Shuo Hsu, Shih‐Jen Hwang, Alex R. Chang, H. Lester Kirchner, Jamie A. Green, Kevin Ho, Angharad Marks, Gordon Prescott, Laura E. Clark, Nick Fluck, Varda Shalev, Gabriel Chodick, Peter J. Blankestijn, Arjan van Zuilen, Jan AJG van den Brand, Mark J. Sarnak, Erwin Böttinger, Girish N. Nadkarni, Stephen G. Ellis, Rajiv Nadukuru, Marie Metzger, Martin Flamant, Pascal Houillier, Jean‐Philippe Haymann, Marc Froissart, Timothy Kenealy, Raina Elley, John Collins, Paul Drury, John K. Cuddeback, Elizabeth L. Ciemins, Rich Stempniewicz, Robert G. Nelson, William C. Knowler, Stephen J. L. Bakker, Rupert Major, James Medcalf, David Shepherd, Elizabeth Barrett‐Connor, Jaclyn Bergstrom, Joachim H. Ix, Miklos Z. Molnar, Keiichi Sumida, Dick de Zeeuw, Barry M. Brenner, Abdul Rashid Qureshi, Carl‐Gustaf Elinder, Björn Runesson, Marie Evans, Mårten Segelmark, Maria Stendahl, Staffan Schön, David Naimark, Navdeep Tangri, Maneesh Sud, Atsushi Hirayama, Kazunobu Ichikawa, Henk J.G. Bilo, Gijs W.D. Landman, Kornelis Jj van Hateren, Nanne Kleefstra, Stein Hallan, Shoshana H. Ballew, Jingsha Chen, Lucia Kwak, Aditya Surapaneni, Hans‐Henrik Parving, Roger A. Rodby, Richard D. Rohde, Julia B. Lewis, Edmund J. Lewis, Ronald D. Perrone, Kaleab Z. Abebe, Fan Fan Hou, Di Xie, Lawrence G. Hunsicker, Enyu Imai, Fumiaki Kobayashi, Hirofumi Makino, Giuseppe Remuzzi, Piero Ruggenenti, Kai‐Uwe Eckardt, Hrefna Guðmundsdóttir, Romaldas Mačiulaitis, Tom Manley, Kimberly Smith, Norman Stockbridge, Aliza Thompson, Thorsten Vetter, Kerry Willis, Luxia Zhang

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of Calgary
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesMayo ClinicMedical Research CouncilNational Institutes of HealthKidney Research UKNational Medical Research CouncilEmory UniversityCleveland ClinicNational Center for Advancing Translational SciencesTufts Medical CenterBristol-Myers SquibbNational Kidney FoundationNational Institute for Health and Care ResearchPKD Foundation
KeywordsAlbuminuriaMedicineKidney diseaseCreatinineInternal medicineRenal functionCohort studyCohort

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.012
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0150.043
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.622
GPT teacher head0.462
Teacher spread0.160 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations320
Published2019
Admission routes1
Has abstractno

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