Classifying Risk in Patients With Chronic Kidney Disease
Bibliographic record
Abstract
Letters19 July 2011Classifying Risk in Patients With Chronic Kidney DiseaseMarcello Tonelli, MD, SM, Paul Muntner, PhD, and Brenda Hemmelgarn, PhD, MDMarcello Tonelli, MD, SMFrom University of Alberta, Edmonton, Alberta T6B 2G3, Canada; University of Alabama at Birmingham, Birmingham, AL 35294; and University of Alberta, Calgary, Alberta T2N 2T9, Canada.Search for more papers by this author, Paul Muntner, PhDFrom University of Alberta, Edmonton, Alberta T6B 2G3, Canada; University of Alabama at Birmingham, Birmingham, AL 35294; and University of Alberta, Calgary, Alberta T2N 2T9, Canada.Search for more papers by this author, and Brenda Hemmelgarn, PhD, MDFrom University of Alberta, Edmonton, Alberta T6B 2G3, Canada; University of Alabama at Birmingham, Birmingham, AL 35294; and University of Alberta, Calgary, Alberta T2N 2T9, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-155-2-201107190-00014 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We agree with Dr. Thorp that it is always preferable to use the most accurate equation available and to use repeated measures of eGFR to classify persons with respect to kidney function. The incremental benefit of using the CKD Epidemiology Collaboration eGFR equation (1) and multiple eGFR measurements spaced at least 3 months apart (2) is worthy of consideration but would be unlikely to affect our conclusion—that incorporating information on proteinuria would improve the prognostic power of the current staging system for CKD.Marcello Tonelli, MD, SMUniversity of AlbertaEdmonton, Alberta T6B 2G3, CanadaPaul Muntner, PhDUniversity of Alabama ...References1. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604-12. [PMID: 19414839] LinkGoogle Scholar2. NKF KDOQI clinical practice guidelines for chronic kidney disease. Am J Kidney Dis. 2002;39 Suppl 1 76. [PMID: 11904577] MedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Marcello Tonelli, MD, SM; Paul Muntner, PhD; Brenda Hemmelgarn, PhD, MDAffiliations: From University of Alberta, Edmonton, Alberta T6B 2G3, Canada; University of Alabama at Birmingham, Birmingham, AL 35294; and University of Alberta, Calgary, Alberta T2N 2T9, Canada.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M10-0837. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoA New Equation to Estimate Glomerular Filtration Rate Andrew S. Levey , Lesley A. Stevens , Christopher H. Schmid , Yaping (Lucy) Zhang , Alejandro F. Castro III , Harold I. Feldman , John W. Kusek , Paul Eggers , Frederick Van Lente , Tom Greene , Josef Coresh , and Using Proteinuria and Estimated Glomerular Filtration Rate to Classify Risk in Patients With Chronic Kidney Disease Marcello Tonelli , Paul Muntner , Anita Lloyd , Braden J. Manns , Matthew T. James , Scott Klarenbach , Robert R. Quinn , Natasha Wiebe , Brenda R. Hemmelgarn , and Classifying Risk in Patients With Chronic Kidney Disease Micah L. Thorp Metrics 19 July 2011Volume 155, Issue 2Page: 134KeywordsChronic kidney diseaseConflicts of interestDisclosureEpidemiologyEstimated glomerular filtration rateInformation technologyKidneysProteinuria ePublished: 19 July 2011 Issue Published: 19 July 2011 Copyright & PermissionsCopyright © 2011 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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".