Bibliographic record
Abstract
For greater than a century, and seemingly even exceeding a millennium, the clinical features of gout have been clearly recognized. These include rapidity of onset, crescendo escalation of joint pain and swelling, association with gluttony and excessive consumption of high purine foods and alcoholic beverages, and predilection for involvement of the first metatarsophalangeal joint (i.e., podagra) at onset of disease1,2. Similarly, demographic aspects about gout are well known: the predominant involvement among men and the abrupt rise in disease incidence among women in the postmenopausal phase of life3,4,5. In contrast, what is relatively new to the field of gout epidemiology is the increasing number of comorbid disorders that are associated with gout incidence and prevalence. In fact, gout is not a condition that lives in isolation. Gout keeps company with many common and highly prevalent chronic medical disorders in contemporary society. For example, among data derived from the National Health and Nutrition Examination Survey 2007–2008, the large majority of Americans afflicted with gout have one or more concomitant comorbidities6. Over 70% of affected men and women have concomitant hypertension and/or compromised renal function; and over half are obese. Approximately one-quarter have diabetes mellitus; the same proportion has a history of nephrolithiasis. Moreover, in the subsequent 2009–2010 survey period, the prevalence of gout steadily rose in association with increasing number of involved comorbidities. As such, the overall prevalence of gout rose from 1.7% among those free of comorbidity, to 4.1% among those who were hypertensive, to 7.0% in those hypertensive with one additional cardiovascular risk factor, and then higher still, to 9.8%, among hypertensive adult Americans with 2 additional cardiovascular risk factors7. Such large population-based surveys also afford the opportunity to observe these comorbid associations among various … Address correspondence to Dr. A.C. Gelber, Johns Hopkins University School of Medicine, 5200 Eastern Ave., Mason F. Lord Bldg., Center Tower, Suite 4100, Baltimore, Maryland 21224, USA; E-mail: agelber{at}jhmi.edu
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".