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Record W4415053754 · doi:10.1016/j.ekir.2025.09.050

CKDs at the Crossroads: From Failures to Future Therapies

2025· article· en· W4415053754 on OpenAlexaff
Mark Elliott, Adeera Levin

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsClinical trialKidney diseaseLimitingGuidelineClinical PracticeAdverse effectMEDLINEDisease

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a public health emergency because it is common and carries an increasing burden of morbidity and mortality. Until recently, treatment options for CKD and its adverse systemic effects were limited; however, we now have multiple approved therapies and a growing pipeline of promising treatments under evaluation. Despite the strong evidence and guideline recommendations supporting the broad use of approved therapies, uptake in practice remains lower than expected, potentially limiting the benefits of these advances. Early identification of CKD remains a prerequisite for therapy, yet screening and cost-effectiveness continue to be debated globally. In this review, we discuss the currently available pharmacologic treatments for CKD that have proven kidney and cardiac benefits with a specific focus on addressing barriers to implementation and ongoing trials that will inform their routine clinical use. We then discuss treatments that are in late-phase clinical trials that may expand the therapeutic options for CKD in the next few years. This will necessitate a personalized approach to management to determine which therapies will work best for which patients. Finally, we touch on therapeutic strategies that did not demonstrate clinical benefits despite rational physiologic support. This review addresses the know-do gap that exists in applying proven therapies for people with CKD and provides clinicians with practical tools and knowledge to improve clinical uptake, while acknowledging global challenges of access and affordability.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.006
GPT teacher head0.280
Teacher spread0.273 · 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
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".

Quick stats

Citations8
Published2025
Admission routes1
Has abstractyes

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