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Record W2026033716 · doi:10.1097/bor.0b013e3282f524a2

Lifestyle and gout

2008· review· en· W2026033716 on OpenAlexaff
A. Elisabeth Hak, Hyon K. Choi

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2008
Typereview
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsArthritis Research Centre of CanadaVancouver General Hospital
Fundersnot available
KeywordsGoutMedicineHyperuricemiaUric acidContext (archaeology)National Health and Nutrition Examination SurveyMetabolic syndromeInternal medicineProspective cohort studyEpidemiologyEnvironmental healthPhysical therapyObesityPopulation

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review summarizes recent epidemiologic research findings on gout, and attempts to put them into the context of clinical and public health decision-making aimed at prevention and improved management of gout. RECENT FINDINGS: A large prospective study found that coffee consumption was inversely associated with risk of gout and that consumption of sugar-sweetened soft drinks or fructose was strongly associated with an increased gout risk. Studies based on the Third National Health and Nutrition Examination Survey (NHANES III) suggest that these consumptions affect serum uric acid levels parallel to the direction of gout risk. Furthermore, data from NHANES III show a remarkably high prevalence of the metabolic syndrome among individuals with gout. Prospective studies found an increased risk of myocardial infarction and cardiovascular mortality in gout patients. SUMMARY: Lifestyle and dietary recommendations for gout patients should consider other health benefits, since gout is often associated with major chronic disorders such as the metabolic syndrome and an increased risk for cardiovascular disease and mortality. In addition to recent dietary recommendations, gout patients should be advised to limit fructose intake. The inverse link between coffee and the risk of gout suggests that coffee could be allowed among gout patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.100
GPT teacher head0.409
Teacher spread0.309 · 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 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

Citations64
Published2008
Admission routes1
Has abstractyes

Explore more

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