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International consensus definitions of clinical trial outcomes for kidney failure: 2020
Adeera Levin, Rajiv Agarwal, William G. Herrington, Hiddo J.L. Heerspink, Johannes F.E. Mann, Shahnaz Shahinfar, Katherine R. Tuttle, Jo‐Ann Donner, Vivekanand Jha, Masaomi Nangaku, Dick de Zeeuw, Meg Jardine, Kenneth W. Mahaffey, Aliza M. Thompson, Mary Beaucage, Kate Chong, Glenda V. Roberts, Duane Sunwold, Hans Vorster, Madeleine Warren, Sandrine Damster, Charu Malik, Vlado Perkovic, Shuchi Anand, Nicholas B. Argent, Elena Babak, Debasish Banerjee, Jonathan Barratt, Aminu K. Bello, Angelito A. Bernardo, Jaime D. Blais, William Canovatchel, Fergus Caskey, Josef Coresh, Ian H. de Boer, Kai‐Uwe Eckardt, Rhys Evans, Harold I. Feldman, Agnes B. Fogo, Hrefna Guðmundsdóttir, Takayuki Hamano, David C.H. Harris, Sibylle J. Hauske, Richard Haynes, Charles A. Herzog, Thomas F. Hiemstra, Thomas Idorn, Lesley A. Inker, Julie H. Ishida, David W. Johnson, Charlotte Jones-Burton, Amer Joseph, Audrey Koitka‐Weber, Matthias Kretzler, Robert Lawatscheck, Adrian Liew, Louise Moist, Saraladevi Naicker, Reiko Nakashima, Uptal D. Patel, Roberto Pecoits‐Filho, Jennifer B. Rose, Noah L. Rosenberg, Marvin Sinsakul, William E. Smoyer, Laura Solá, Amy R. Sood, Bénédicte Stengel, Maarten W. Taal, Mototsugu TANAKA, Marcello Tonelli, Allison Tong, Robert Toto, Michele Trask, Ifeoma Ulasi, Christoph Wanner, David C. Wheeler, Benjamin Ole Wolthers, Harold M. Wright, Yoshihisa Yamada, Elena Zakharova
Bibliographic record
VenueKidney International · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsCanadian Association of Nurses in OncologyKidney Foundation of CanadaC17 CouncilOntario Stroke NetworkUniversity of British ColumbiaProvidence Health Care
FundersNational Heart, Lung, and Blood InstituteRelypsaChugai PharmaceuticalGlaxoSmithKlineUniversity of California, San FranciscoBaxter Healthcare CorporationCanadian Institutes of Health ResearchPfizerUniversity of California, Los AngelesKorean Society of NephrologyOtsuka PharmaceuticalEli Lilly and CompanyUniversity of British ColumbiaUniversity of OxfordCelgeneKidney Research UKAngionNxStageSatellite HealthcareServierMedical Research CouncilVerily Life SciencesFresenius Medical Care North AmericaAstraZeneca CanadaIronwood Pharmaceuticals, IncorporatedAstellas PharmaBayerNational Institute of Diabetes and Digestive and Kidney DiseasesGilead SciencesNewton FundInternational Society of NephrologyNational Institutes of HealthSociété de NéphrologieRegeneron PharmaceuticalsNovo NordiskMyoKardiaOmeros CorporationBoston Scientific CorporationAblynxSanofiAmgenDaiichi Sankyo EuropeInternational Seafood Sustainability FoundationCSL BehringNational Health and Medical Research CouncilAstraZenecaAkebia TherapeuticsBristol-Myers Squibb
KeywordsMedicineIntensive care medicineClinical trialRandomized controlled trialDialysisRenal functionStakeholderInternal medicinePolitical sciencePublic relations
Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.999
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 head0.116
GPT teacher head0.386
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation status
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 teacher head, not a consensus.
Study designNot applicable
Domainnot available
GenreEmpirical
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
Citations118
Published2020
Admission routes2
Has abstractno