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Record W2169777250 · doi:10.1111/hdi.12313

Febuxostat improves endothelial function in hemodialysis patients with hyperuricemia: A randomized controlled study

2015· article· en· W2169777250 on OpenAlexvenueno aff
Yuki Tsuruta, Kan Kikuchi, Yukio Tsuruta, Yuko Sasaki, Takahito Moriyama, Mitsuyo Itabashi, Takashi Takei, Keiko Uchida, Takashi Akiba, Ken Tsuchiya, Kosaku Nitta

Bibliographic record

VenueHemodialysis International · 2015
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsFebuxostatHyperuricemiaMedicineHemodialysisInternal medicineEndothelial dysfunctionBlood pressureAllopurinolMalondialdehydeUric acidOxidative stress

Abstract

fetched live from OpenAlex

Endothelial dysfunction is often found in both hyperuricemia and hemodialysis patients. Recent studies have shown that treating hyperuricemia with allopurinol improves endothelial dysfunction. This study is performed to assess the effect of febuxostat on endothelial dysfunction in hemodialysis patients with hyperuricemia. We randomly assigned 53 hemodialysis patients with hyperuricemia to a febuxostat (10 mg daily) group and a control group and measured flow-mediated dilation, serum uric acid (UA) levels, systolic and diastolic blood pressure, malondialdehyde-modified low-density lipoprotein (MDA-LDL), and highly sensitive C-reactive protein (hsCRP) at baseline and at the end of a 4-week study period. Flow-mediated dilation increased from 5.3% ± 2.4% to 8.9% ± 3.6% in the febuxostat group but did not change significantly in the control group. Treatment with febuxostat resulted in a significant decrease in serum UA level and a significant decrease in MDA-LDL compared with baseline, but no significant difference was observed in hsCRP level or blood pressure. No significant differences were observed in the control group. Febuxostat improved endothelial dysfunction and reduced serum UA levels and oxidative stress in hemodialysis patients with hyperuricemia.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.244
Teacher spread0.233 · 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 designRandomized trial
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

Citations50
Published2015
Admission routes1
Has abstractyes

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