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Record W4412441457 · doi:10.1016/j.pcd.2025.05.005

A primary care guide to the screening and pharmacologic management of chronic kidney disease in people living with type 2 diabetes

2025· review· en· W4412441457 on OpenAlexafffund
Eugene E. Wright, Ana María Cebrián Cuenca, Daniel Ngui

Bibliographic record

VenuePrimary care diabetes · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of British Columbia
FundersNovo NordiskCanadian Cardiovascular SocietyAstraZenecaBayerDiabetes CanadaEli Lilly and Company
KeywordsMedicinePrimary careDiabetes mellitusKidney diseaseType 2 diabetesChronic diseaseIntensive care medicineDiseaseDisease managementFamily medicineGerontologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

This paper reports the expert opinions and recommendations made by primary care physicians (PCPs) to optimize screening and management of chronic kidney disease (CKD) associated with diabetes and presents algorithms to provide a practical and simplified guide for PCPs. Individuals living with type 2 diabetes (T2D) should be screened early and at regular intervals for CKD using both estimated glomerular filtration rate and urinary albumin-to-creatinine ratio testing. The risk of CKD assessed using the Kidney Disease: Improving Global Outcomes heatmap should be reviewed at least annually to optimize treatment to slow progression of CKD. Lifestyle modifications form the foundation of reducing CKD risk in individuals with T2D. A pillared approach to pharmacotherapy (renin-angiotensin system inhibitors, sodium-glucose cotransporter 2 inhibitors, a nonsteroidal mineralocorticoid receptor antagonist [finerenone], and glucagon-like peptide-1 receptor agonists) is recommended in individuals with CKD and T2D.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.011
GPT teacher head0.281
Teacher spread0.270 · 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 designNot applicable
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

Citations3
Published2025
Admission routes2
Has abstractyes

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