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Record W2153880890 · doi:10.3899/jrheum.141031

Erectile Dysfunction Is Common among Patients with Gout

2015· article· en· W2153880890 on OpenAlexvenueno aff
Naomi Schlesinger, Diane C. Radvanski, Jerry Cheng, John B. Kostis

Bibliographic record

VenueThe Journal of Rheumatology · 2015
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGoutErectile dysfunctionShim (computing)Internal medicineRheumatologyPhysical therapyDiabetes mellitusSurgeryEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine whether men with gout may have an increased prevalence of erectile dysfunction (ED) as compared with men without gout. METHODS: In this cross-sectional study, men aged 18-89 presenting to the rheumatology clinic between August 26, 2010, and May 13, 2013, were asked to participate. The presence of ED was determined by the Sexual Health Inventory in Men (SHIM). SHIM classifies ED into 1 of 5 categories: absent (22-25), mild (17-21), mild to moderate (12-16), moderate (8-11), and severe (1-7). Patient's history, physical examination, and recent laboratory studies were reviewed as well. Descriptive statistics and subgroup analyses were used to summarize the data. RESULTS: Of the 201 men surveyed, 83 had gout (control, n = 118). A significantly greater proportion of patients with gout (63, 76%) had ED versus patients without gout (60, 51%, p = 0.0003). A significantly greater proportion of patients with gout (22, 26%) had severe ED versus patients without gout (17, 15%, p = 0.04). Patients with gout had an average SHIM score of 14.4 versus 18.48 in patients without gout (p < 0.0001). There was a statistically significant association between gout and ED. The association remained significant after adjustment for age, hypertension, diabetes, and obesity. CONCLUSION: ED is present in most men with gout and is frequently severe. We propose that patients with gout be routinely screened for ED.

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 score0.023
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.234
Teacher spread0.222 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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