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Record W1968229581 · doi:10.3109/10408363.2012.736470

Adiponectin in renal disease – a review of the evidence as a risk factor for cardiovascular and all-cause mortality

2012· review· en· W1968229581 on OpenAlexaff
Philip W. Connelly, G. V. Ramesh Prasad

Bibliographic record

VenueCritical Reviews in Clinical Laboratory Sciences · 2012
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAdiponectinAdipokineMedicineInternal medicineDiabetes mellitusDiseaseRenal functionKidney diseaseRisk factorEndocrinologyType 2 diabetesCardiologyInsulin resistance

Abstract

fetched live from OpenAlex

Adiponectin, an adipokine, was discovered in 1995. The initial evidence led to the study of adiponectin as a determinant of insulin sensitivity and blood glucose levels. The literature then evolved to reports of the inverse association of adiponectin with incident Type 2 diabetes mellitus and coronary heart disease. Shortly thereafter, reports of a positive association with heart failure and mortality appeared and were replicated. We review here the basic science evidence and clinical studies of the role of renal function and kidney disease as a determinant of adiponectin.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.349
GPT teacher head0.526
Teacher spread0.176 · 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 designSystematic review
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

Citations5
Published2012
Admission routes1
Has abstractyes

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