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Record W2317733955 · doi:10.1097/mnh.0000000000000214

New and old agents in the management of diabetic nephropathy

2016· review· en· W2317733955 on OpenAlexafffund
Yuliya Lytvyn, Petter Bjornstad, Nicole Pun, David Z.I. Cherney

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2016
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health Research
KeywordsMedicineDiabetic nephropathyNephropathyDiabetes mellitusTubuloglomerular feedbackPharmacologyBioinformaticsInternal medicineKidneyEndocrinology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Diabetic nephropathy is a long-standing complication of diabetes mellitus and is responsible for more than 40% of end-stage renal disease cases in developed countries. Unfortunately, conventional renin-angiotensin-aldosterone system (RAAS) inhibitor medications only partially protect against the development and progression of diabetic nephropathy. Moreover, RAAS inhibitors have failed as primary prevention therapy in type 1 diabetes. Thus, agents targeting alternative pathogenic mechanisms leading to diabetic nephropathy have been intensively investigated, which is the topic of this review. RECENT FINDINGS: Promising emerging agents have targeted neurohormonal activation (alternative components of the RAAS and neprilysin inhibition), tubuloglomerular feedback mechanisms (sodium glucose cotransporter 2 inhibition and incretin-based therapy) and renal inflammation/fibrosis. SUMMARY: Evidence demonstrating the potential of these agents to protect and prevent progression of diabetic nephropathy is summarized in this review. There are dedicated clinical trials ongoing with these therapies, which have the potential to change the clinical practice.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.100
GPT teacher head0.360
Teacher spread0.259 · 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 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

Citations34
Published2016
Admission routes2
Has abstractyes

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