← Back to cohort
CureGN Study Rationale, Design, and Methods: Establishing a Large Prospective Observational Study of Glomerular Disease
Laura Mariani, Andrew S. Bomback, Pietro A. Canetta, Michael F. Flessner, Margaret Helmuth, Michelle Hladunewich, Jonathan Hogan, Krzysztof Kiryluk, Patrick H. Nachman, Cynthia C. Nast, Michelle N. Rheault, Dana V. Rizk, Howard Trachtman, Scott E. Wenderfer, Corinna Bowers, Peg Hill-Callahan, Maddalena Marasà, Caroline J. Poulton, Adelaide Revell, Suzanne Vento, Laura Barisoni, D C Cattran, Vivette D. D’Agati, J. Charles Jennette, Jon B. Klein, Louis‐Philippe Laurin, Katherine Twombley, Ronald J. Falk, Ali G. Gharavi, Brenda W. Gillespie, Debbie S. Gipson, Larry A. Greenbaum, Lawrence B. Holzman, Matthias Kretzler, Bruce Robinson, William E. Smoyer, Lisa M. Guay‐Woodford, Wooin Ahn, Gerald B. Appel, Revekka Babayev, Ibrahim Batal, Eric J. Brown, Eric S. Campenot, Lucrezia Carlassara, Brenda Chan, Debanjana Chatterjee, Elisa Delbarba, Samriti Dogra, Hilda Fernández, Bartosz Foroncewicz, Gian Marco Ghiggeri, William H. Hines, S. Ali Husain, Namrata G. Jain, Pascale Khairallah, Byum Hee Kil, Anushya Jeyabalan, Wai Lam Lau, Fangming Lin, Francesca Lugani, Glen S. Markowitz, Sumit Mohan, Xueru Mu, Krzysztof Mucha, Thomas L. Nickolas, Stacy Piva, Jai Radhakrishnan, Maya K. Rao, Renu Regunathan-Shenk, Simone Sanna‐Cherchi, Dominick Santoriello, Shayan Shirazian, Michael B. Stokes, Natalie Yu, Anthony M. Valeri, Ronald Zviti, Amira Al‐Uzri, Josephine M. Ambruzs, Isa Ashoor, Diego Avilés, Rossana Baracco, John Barcia, Sharon Bartosh, Craig W. Belsha, Michael Braun, Yi Cai, Vladimir Chernitskiy, Aftab S. Chishti, Donna Claes, Kira Clark, Carl H. Cramer, Keefe Davis, Amy Dutcher, Elif Erkan, Daniel I. Feig, Michael Freundlich, Joseph P. Gaut, Rasheed Gbadegesin, Melisha Hanna, Guillermo Hidalgo, David K. Hooper, Tracy E. Hunley, Amrish Jain, Mahmoud Kallash, Margo Kamel, Myda Khalid, Theresa Kump, Jerome C. Lane, Helen Liapis, John D. Mahan, Nisha Mathews, Carla Nester, Cynthia G. Pan, Larry T. Patterson, Hiren P. Patel, Alice A. Raad, Cynthia Silva, Rajasree Sreedharan, Tarak Srivastava, Julia Steinke, Susan Sumner, Tetyana L. Vasylyeva, Chia-shi Wang, Donald J. Weaver, Craig S. Wong, Hong Yin, Anand Achanti, Salem Almaani, Isabelle Ayoub, Milos N. Budisavljevic, Maggie D’Angelo, Vimal K. Derebail, Huma Fatima, Agnes B. Fogo, Keisha L. Gibson, Dorey A. Glenn, Susan L. Hogan, Koyal Jain, Bruce A. Julian, Jason M. Kidd, H. Davis Massey, Amy K. Mottl, Shannon L. Murphy, Tibor Nádasdy, Jan Novák, Samir M. Parikh, Thomas Brian Powell, Bryce B. Reeve, Matthew B. Renfrow, Monica L. Reynolds, Brad H. Rovin, Virginie Royal, Manish K. Saha, Neil Sanghani, Sally Self, Sharon G. Adler, Nada Alachkar, Charles E. Alpers, Raed Bou Matar, Carmen Ávila-Casado, Serena M. Bagnasco, Emily Brede, Elizabeth Brown, Daniel C. Cattran, Michael Choi, Gabriel Contreras, Katherine M. Dell, Darren A. DeWalt, Michelle Denburg, Ram Dukkipati, Fernando C. Fervenza, Alessia Fornoni, Crystal A. Gadegbeku, Patrick E. Gipson, Anny Gonzalez-Zea, Leah Hasely, Elizabeth Hendren, Sangeeta Hingorani, Jean Hou, J. Ashley Jefferson, Kenar D. Jhaveri, Duncan B. Johnstone, Frederick J. Kaskel, Amy Kogan, Jeffrey B. Kopp, Richard Lafayette, Kevin V. Lemley, Laura Málaga-Diéguez, Kevin Meyers, Alicia M. Neu, Michelle M. O’Shaughnessy, John F. O’Toole, Andrea L. Oliverio, Matthew Palmer, Rulan S. Parekh, Renée Pitter, Heather N. Reich, Kimberly J. Reidy, Helbert Rondon‐Berrios, Kamalanathan K. Sambandam, Matthew G. Sampson, John R. Sedor, David T. Selewski, Christine B. Sethna, Jeffrey R. Schelling, Katherine R. Tuttle, Meryl Waldman, Joseph Weisstuch, Roger C. Wiggins, David Williams, Cheryl A. Winkler, Eric W. Young, Olga Zhdanova, Charlotte A. Beil, Richard Eikstadt, John Graff, Stephen M. Hewitt, Emily Herreshoff, Chrysta Lienczewski, Sarah Mansfield, Keith McCullough, Nicholas Moore, Melissa Sexton, Jonathan P. Troost, Matthew Wladkowski, Jarcy Zee, Dawn Zinsser
Bibliographic record
VenueAmerican Journal of Kidney Diseases · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsHôpital Maisonneuve-RosemontUniversity Health NetworkHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of Health
KeywordsMedicineObservational studyProspective cohort studyResearch designDiseaseIntensive care medicineInternal medicineStatistics
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 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.
metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.041
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: Observational
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.246
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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.
Opus teacher head0.038
GPT teacher head0.365
Teacher spread0.326 · 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 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
GenreProtocol
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
Citations136
Published2018
Admission routes1
Has abstractno