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Record W2956359130 · doi:10.1111/dom.13831

Renal hyperfiltration defined by high estimated glomerular filtration rate: A risk factor for cardiovascular disease and mortality

2019· review· en· W2956359130 on OpenAlexaff
Mehmet Kanbay, Lale A. Ertuğlu, Barış Afşar, Elif Ozdogan, Zeynep S. Kucuksumer, Alberto Ortíz, Adrian Covic, Masanari Kuwabara, David Z.I. Cherney, Daniël H. van Raalte, Dick de Zeeuw

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2019
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersKoç Üniversitesi Translasyonel Tıp Araştırma MerkeziMinisterio de Fomento
KeywordsGlomerular hyperfiltrationRenal functionMedicineKidney diseaseDiabetes mellitusNephronRisk factorDiseaseInternal medicineIntensive care medicineEndocrinologyDiabetic nephropathy

Abstract

fetched live from OpenAlex

Renal hyperfiltration, defined as an increased glomerular filtration rate above normal values, is associated with early phases of kidney disease in the setting of various conditions such as obesity and diabetes. Although it is recognized that glomerular hyperfiltration, that is, increased filtration per nephron unit (usually studied at low glomerular filtration levels and often referred to as single nephron hyperfiltration), is a risk factor for the progression of chronic kidney disease, the implications of having renal hyperfiltration for cardiovascular disease and mortality risk are incompletely understood. Recent evidence from diverse populations, including healthy individuals and patients with diabetes or established cardiovascular disease, suggests that renal hyperfiltration is associated with a higher risk of cardiovascular disease and all-cause mortality. In this review, we critically summarize the existing studies, discuss possible mechanisms, and describe the remaining gaps in our knowledge regarding the association of renal hyperfiltration with cardiovascular disease and mortality risk.

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 distilled prediction

Teacher imitation

Not 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.884
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.287
Teacher spread0.252 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations96
Published2019
Admission routes1
Has abstractyes

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