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Record W2610286323 · doi:10.1016/j.jare.2017.04.006

Treatment of asymptomatic hyperuricemia in chronic kidney disease: A new target in an old enemy – A review

2017· review· en· W2610286323 on OpenAlexaff
Maria Erika Ramirez, Joanne M. Bargman

Bibliographic record

VenueJournal of Advanced Research · 2017
Typereview
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsHyperuricemiaAsymptomaticUric acidMedicineKidney diseaseRenal functionKidneyDiseaseInternal medicineGoutEndocrinologyUrology

Abstract

fetched live from OpenAlex

Asymptomatic hyperuricemia is increasing in prevalence. There is a growing body of literature suggesting that uric acid has deleterious effects on vascular health and renal histological integrity. Several trials, reviewed herein, suggest that lowering the serum uric acid level is associated with a slowing in the rate of renal deterioration in those with chronic kidney disease. Given that there is little available in the general armamentarium to slow the rate of kidney deterioration, strong consideration could be given to the administration of agents or lifestyle changes that decrease uric acid production in hyperuricemic patients with deteriorating kidney function.

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 score0.003
Threshold uncertainty score0.010

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 science0.0010.000
Research integrity0.0010.002
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 head0.170
GPT teacher head0.505
Teacher spread0.335 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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