Bibliographic record
Abstract
From ancient times to the present day, gout has been associated in the popular and scientific literature with wealthy men who overindulge in fancy foods, fine wine, and debauchery. Curiously, amongst diseases, gout was thought to be good, a malady to be accepted because of otherwise beneficial effects on health, and longevity. This narrative review critically examines the history of these associations and explores in detail the pathogenic factors contributing to development of gout prior to the 20th century. While lead toxicity has been previously implicated with wine, the specific association of gout and fine wine can be attributed to lead complexes in products such as sapa, a grape extract used to sweeten wine, in addition to lead nanoparticles leached from crystal glassware and lead glazed dinner plates. The health benefits of gout can be attributed to lead complexes in fine wine and lead nanoparticles from glazed dinnerware. These compounds have excellent antibacterial properties, thereby inhibiting the presence of pathogenic bacteria in foodstuffs. Probing the association of gout and fine wine provides a very well documented example of how the pathogenesis of disease becomes better understood with the passage of time and continuing, persistent scientific enquiry.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".