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Record W2038248980

Association of Hyperuricemia With Carotid Intima-Media Thickness, Albuminuria, Diabetes, Hypertension in Chronic Renal Failure

2012· article· en· W2038248980 on OpenAlexvenueno aff
Jayanta Paul

Bibliographic record

VenueWorld Journal of Nephrology and Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
Fundersnot available
KeywordsHyperuricemiaMedicineInternal medicineDiabetes mellitusAlbuminuriaIntima-media thicknessDiabetic nephropathyCardiologyUric acidGastroenterologyEndocrinologyCarotid arteries
DOInot available

Abstract

fetched live from OpenAlex

Background: Carotid artery intima-media thickness (CAIMT) measured by B-mode ultrasonography is widely used as a surrogate marker atherosclerosis. Hyperuricemia is also a well recognized risk factor for cardiovascular diseases. This study was done to find out the role of hyperuricemia on CAIMT and albuminuria, diabetes and hypertension in chronic renal failure (CRF) patients. Methods: 132 CRF patients and 66 age and sex matched healthy controls were included in this study. Out of 132 CRF patients, 35 were hyperuricemic. CAIMT were measured by B-mode ultrasonography. Statistical analyses were done by SPSS (Statistical package for the social Sciences) soft ware (window version 17.0). Results: Hyperuricemia was independently correlated with CAIMT in CRF patients (P = 0.018). CRF patients with hyperuricemia had significantly higher CAIMT compared to CAIMT of CRF patients without hyperuricemia (P value < 0.001) and healthy controls (P < 0.001). In hyperuricemic CRF patients, prevalence of hypertension (P < 0.001) and diabetes (P = 0.007) was significantly higher than non-hyperuricemic CRF patients.  Conclusions: Hyperuricemia is independently responsible for increased thickening of Carotid artery intima media thickness and also associated with higher level of albuminuria, and higher prevalence of hypertension and diabetes in CRF patients. doi:10.4021/wjnu14w

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.202
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2012
Admission routes1
Has abstractyes

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Same venueWorld Journal of Nephrology and UrologySame topicCardiovascular Disease and AdiposityFrench-language works237,207