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Record W4283157454 · doi:10.52403/ijrr.20220601

Prehyperuricemia: New Milestone in Metabolic Disorders

2022· article· en· W4283157454 on OpenAlexaff
G R Subbu, Anita N., K Hari

Bibliographic record

VenueInternational Journal of Research and Review · 2022
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsASTER
Fundersnot available
KeywordsHyperuricemiaMedicineUric acidGoutMetabolic syndromeInternal medicineFebuxostatMetabolic disorderEndocrinologyPhysiologyDiabetes mellitus

Abstract

fetched live from OpenAlex

The global population is living amid a metabolic explosion. The prevalence of hyperuricemia, as a metabolic disorder and a causal agent of non-communicable diseases, has been gearing up rapidly worldwide during the last two decades due to consuming a high purine diet, alcohol, red meat, high fructose-containing food, and lifestyle changes. The invisible bond between hyperuricemia and many non-communicable diseases is more robust than before. During the evolution of hyperuricemia, systemic inflammation develops, leading to endothelial dysfunction and end-organ injury. These molecular changes were not recognized previously. Hyperuricemia is now a metabolic, more clearly a vascular disorder than a crystallization disease. Asymptomatic hyperuricemia is no more benign, and gout is not synonym with hyperuricemia or vice versa. Diagnose hyperuricemia in an early stage at a high normal level and control it to prevent the development and complications of many hyperuricemia-related extra-articular diseases. For more acceptance and importance, this high normal level of serum uric acid can be named prehyperuricemia. As in the case of prediabetes and prehypertension, prehyperuricemia should be diagnosed early irrespective of age and sex; preventive measures have to be taken and maintain uric acid at a safer level. keywords: Serum uric acid, hyperuricemia, prehyperuricemia, high normal value of serum uric acid, metabolic disorder, non-communicable disorder, and molecular mechanism.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.771
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.427
Teacher spread0.375 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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