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Record W1977389343 · doi:10.1097/mnh.0b013e3283293db1

Early nephropathy in type 1 diabetes: the importance of early renal function decline

2009· review· en· W1977389343 on OpenAlexafffund
Bruce A. Perkins, Andrzej S. Królewski

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2009
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of Toronto
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthCanadian Diabetes Association
KeywordsMicroalbuminuriaMedicineRenal functionNephropathyCystatin CProteinuriaDiabetic nephropathyDiabetes mellitusType 2 diabetesAlbuminuriaInternal medicineKidney diseaseEndocrinologyKidney

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The results of recent clinical trials in early diabetic nephropathy demonstrate that current therapies designed to suppress microalbuminuria do not prevent renal function decline. However, recent observational studies refined the traditional model of early nephropathy in type 1 diabetes and may inform more effective therapies for the prevention of chronic kidney disease. RECENT FINDINGS: A contemporary model of early nephropathy in type 1 diabetes has emerged in which initiation of renal function decline occurs soon after the onset of microalbuminuria and is not conditional on progression to proteinuria. Early renal function decline can be diagnosed using serial measurement of serum cystatin C. Abnormal levels of markers of protein glycation, uric acid metabolism, and chronic inflammation appear to represent mechanisms unique to early renal function decline and distinct from those involved in microalbuminuria. SUMMARY: Recent findings refine the existing paradigm of early nephropathy in type 1 diabetes and have significant implications for research. Clinical tests--such as an algorithm for the serial determination of serum cystatin C--should be developed for monitoring early renal function decline for use as an outcome in clinical trials.

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)0.000
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 score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0010.001
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 head0.071
GPT teacher head0.344
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 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

Citations62
Published2009
Admission routes2
Has abstractyes

Explore more

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