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Record W2953333833 · doi:10.1093/ndt/gfz103.sp271

SP271GFR MEASUREMENT SAMPLING STRATEGY: A CRITICAL ELEMENT IN GFR DETERMINATION AT ALL LEVELS OF GFR

2019· article· en· W2953333833 on OpenAlexaff
Christine A. White, Celine Allen, Ayub Akbari, Andrew G. Day, Mandy E. Turner, Greg Knoll

Bibliographic record

VenueNephrology Dialysis Transplantation · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of OttawaQueen's University
Fundersnot available
KeywordsMedicineSampling (signal processing)Renal functionUrologyInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Urinary inulin clearance is considered the gold standard of GFR measurement but plasma clearance protocols using more accessible and inexpensive iohexol and 99mTc-DTPA are far more commonly performed. Many different plasma sampling protocols exist (number of and timing of samples) but very little is known about the accuracy of these in general and in patients with specific characteristics. The aim of this study is to compare GFR determined by plasma clearance of iohexol and 99mTc-DTPA with varying sampling strategies against gold standard urinary inulin GFR and to identify sampling protocols with the greatest accuracy in eGFR subgroups and in patients with significant edema. METHODS: GFR was measured using urinary inulin clearance, plasma iohexol clearance, and plasma 99mTc DTPA clearance simultaneously in 77 subjects with known chronic kidney disease. Blood samples were collected at several timepoints to allow for evaluation of different sampling protocols. For each method, mean bias (alternate GFR - inulin GFR), precision (standard deviation of mean bias), and accuracy (the percent of alternate measures within 10% (P10) and 30% (P30) of inulin GFR were calculated for the entire cohort and for eGFR –EPI Cr subgroups (<30, 30-59 and ≥ 60 ml/min/1.73m2) and edema subgroups (1&2, 3&4). RESULTS: Mean age 62.3 ± 12.6 yrs, BSA 2.0 ± 0.3 m2, 95% white race, 40% female sex. Mean inulin GFR 33 ± 19 ml/min/1.73m2. For eGFR less than 30 ml/min/1.73m2, the most accurate protocol included samples between 4-10 hours (P30= 76%). The 2-10 hour protocol was less accurate (P30=60%). For eGFR greater than 60 ml/min/1.73 m2, the most accurate protocol included samples between 2-4 hours (P10=70%). More prolonged sampling (2-10 hours) lead to reduced accuracy (P10=50%). For all iohexol protocols, inulin GFR was overestimated to a greater degree in those with significant edema (stages 3&4) as compared to those without significant edema (stages 1&2). For those with signficant edema, the most accurate protocol (P30=64) had delayed intial and final samples (4-10 hours). CONCLUSIONS: Delay of both initial and final samples in iohexol plasma clearance protocols significantly increases GFR measurement accuracy in those with low GFR and significant edema. Early samples yield the best accuracy in those with higher GFRs. These results reveal the need for individualized GFR measurement protocols based on patient characteristics including GFR and edema status.

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)0.035
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.310
Teacher spread0.267 · 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
Published2019
Admission routes1
Has abstractyes

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