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Record W4390483354 · doi:10.7759/cureus.51438

Gouty Toes and Rosacea Nose: Does Enlightenment-Era Art Suggest a Correlation?

2024· editorial· en· W4390483354 on OpenAlexaff
Mohammed Abrahim

Bibliographic record

VenueCureus · 2024
Typeeditorial
Languageen
FieldMedicine
TopicAcne and Rosacea Treatments and Effects
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRosaceaEnlightenmentGoutMedicineDermatologyAestheticsArtInternal medicinePhilosophy

Abstract

fetched live from OpenAlex

Gout, one of the most ancient documented diseases in history, has long captivated artists, yielding a rich collection of artworks. This interest peaked during the Enlightenment era in Europe, a time marked by a surge in gout cases alongside rising wealth, consumerism, and subsequent increased public access to artists. This editorial aims to highlight an intriguing observation of a novel association within several Enlightenment-era paintings depicting individuals suffering from gout and often also portraying the distinctive red noses and cheeks seen in patients with rosacea. Traditionally, both rosacea and gout have been classified as localized inflammatory diseases. However, recent studies challenge this conventional categorization, suggesting that these conditions might be components of systemic inflammatory syndromes. Despite the widespread prevalence of these conditions, their potential interconnectedness and shared pathophysiological pathways remain unexplored. Therefore, the representation of gout and rosacea in historical art could extend beyond mere artistic interest, offering a unique and critical perspective for contemporary medical research.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0070.005

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 head0.004
GPT teacher head0.264
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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