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Record W2895811679 · doi:10.1016/j.kint.2018.08.009

Grams ME, Sang Y, Ballew SH, et al, for the Chronic Kidney Disease Prognosis Consortium. Predicting timing of clinical outcomes in patients with chronic kidney disease and severely decreased glomerular filtration rate. Kidney Int. 2018;93:1442–1451

2018· erratum· en· W2895811679 on OpenAlexfundno aff
Brad C. Astor, Adeera Levin, Mila Tang, Ognjenka Djurdjev, Sankar D. Navaneethan, Stacey E. Jolly, Jesse D. Schold, Joseph V. Nally, Jonathan Emberson, John Townend, Martin Landray, Harold I. Feldman, Chi‐yuan Hsu, James Lash, Philip A. Kalra, James Ritchie, Maharajan Raman, Rachel Middleton, Donal O’Donoghue, Kai‐Uwe Eckardt, Markus P. Schneider, Anna Köttgen, Florian Kronenberg, Barbara Bärthlein, Jamie Green, H. Lester Kirchner, Kevin Ki‐Wai Ho, Angharad Marks, Corri Black, Gordon Prescott, Nick Fluck, Masaaki Nakayama, Mariko Miyazaki, Tae Yamamoto, Gen Yamada, Angela Yee‐Moon Wang, Sharon Cheung, Sharon Wong, Jessie Chu, Henry H. L. Wu, Amit Garg, Eric McArthur, Danielle M. Nash, Varda Shalev, Gabriel Chodick, Peter J. Blankestijn, Jack F.M. Wetzels, Arjan van Zuilen, Jan A.J.G. van den Brand, Lesley Inker, Mark J. Sarnak, Hocine Tighiouart, Haitao Zhang, Bénédicte Stengel, Marie Metzger, Martin Flamant, Pascal Houillier, Jean‐Philippe Haymann, Pablo Ríos, Nelson Mazzuchi, Liliana Gadola, Verónica Lamadrid, Laura Solá, John Collins, C. Raina Elley, Timothy Kenealy, Olivier Moranne, Cécile Couchoud, Cécile Vigneau, Nigel J. Brunskill, Rupert Major, David Shepherd, James Medcalf, Csaba P. Kövesdy, Kamyar Kalantar‐Zadeh, Miklos Z. Molnar, Keiichi Sumida, Praveen K. Potukuchi, Hiddo J.L. Heerspink, Dick de Zeeuw, Barry E. Brenner, Juan Jesús Carrero, Alessandro Gasparini, Abdul Rashid Qureshi, Carl‐Gustaf Elinder, Frank L.J. Visseren, Yolanda van der Graaf, Marie Evans, Maria Stendahl, Staffan Schön, Mårten Segelmark, Karl‐Göran Prütz, David Naimark, Navdeep Tangri, Patrick B. Mark, Jamie P. Traynor, Colin Geddes, Peter Thomson, Ron T. Gansevoort, Morgan E. Grams, Kunihiro Matsushita, Mark Woodward, Luxia Zhang, Shoshana H. Ballew, Jingsha Chen, Lucia Kwak, Yingying Sang, Aditya Surapaneni, Brenda R. Hemmelgarn, Wolfgang C. Winkelmayer­, John M. Davis, Danielle Green, Michael Cheung, Tanya Green, Melissa Mcmahan

Bibliographic record

VenueKidney International · 2018
Typeerratum
Languageen
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
FundersKidney Research UKNational Institute of Diabetes and Digestive and Kidney DiseasesInstitute for Clinical Evaluative SciencesCleveland Clinic
KeywordsMedicineKidney diseaseDialysisRenal functionRenal replacement therapyCohortInternal medicineGerontology

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)0.007
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.108
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.001
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0320.019

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 head0.038
GPT teacher head0.360
Teacher spread0.322 · 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 designObservational
Domainnot available
GenreOther

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

Citations9
Published2018
Admission routes1
Has abstractno

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