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Record W3173157158 · doi:10.2337/db21-13-lb

13-LB: Measured vs. Estimated GFR in Young Adults with Uncomplicated T1D

2021· article· en· W3173157158 on OpenAlexaff
Karolina Gaebe, Christine A. White, Farid H. Mahmud, James W. Scholey, Laura Motran, Marvilyn Palaganas, Yesmino Elia, David Cherney, Etienne Sochett

Bibliographic record

VenueDiabetes · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicineRenal functionUrologyCreatininePopulationKidney diseaseInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Background: eGFR equations are used to monitor progression of kidney disease, but their performance is incompletely understood in young adults with T1D and preserved GFR. To address this deficit, we compared two eGFR equations (creatinine-based CKD-EPI and modified Schwartz) to mGFR in this population.Methods: GFR based on plasma iohexol clearance was measured in 53 patients (24 males) with T1D, as part of the CAN-SOLVE CKD studyResults: Mean age was 20.6±2.1 yrs with mean diabetes duration of 13.1±3.3 yrs. Mean HbA1c and ACR were 8.1±1.3 and 1.6±1.9 mg/mmol, respectively. Mean mGFR was 107.6±15.9 ml/min/1.73 m2. The Schwartz eGFR had a smaller mean bias (5.6 vs. 27.4 ml/min/1.73m2), better accuracy, and overall correlation compared to the CKD-EPI eGFR, while the latter had slightly superior precision. However, both equations were in poor agreement with mGFR. Examination of the Bland Altman plots reveals that, in the lower normal range, the CKD-EPI equation overestimated mGFR. The modified Schwartz equation showed no discernible tendencies.Conclusion: In young adults with T1D and preserved GFR, the modified Schwartz eGFR performed slightly better than CKD-EPI eGFR. Overall, however, both were in poor agreement with mGFR suggesting caution about the use of eGFR as a surrogate outcome in longitudinal studies and emphasizing the need to develop novel equations for this population.View largeDownload slideView largeDownload slide DisclosureK. Gaebe: None. C. A. White: None. F. H. Mahmud: Advisory Panel; Self; Insulet Corporation, Lilly Diabetes. J. W. Scholey: None. L. Motran: None. M. Palaganas: None. Y. T. Elia: None. D. Cherney: Other Relationship; Self; AbbVie Inc., AstraZeneca, Bayer AG, Boehringer Ingelheim International GmbH, Janssen Scientific Affairs, LLC., Lilly Diabetes, Merck & Co., Inc., Mitsubishi-Tanabe, Maze Inc, Prometic, Novo Nordisk, Sanofi. E. B. Sochett: None.FundingCanadian Institutes of Health Research (20R26070); JDRF

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.002
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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