Bibliographic record
Abstract
Gout is a common cause of acute painful arthritis with a prevalence of 3.9% of adults in the United States, affecting an estimated 8.3 million people1. Gout prevalence increases with age, affecting 12.6% of those aged 80 and over1. Untreated and undertreated gout progresses to a chronic disabling arthropathy in a significant number of patients2. An increasing proportion of patients with gout have complex profiles of comorbidities and long prescription medication lists further complicating their management3. In this setting the recent publication of the American College of Rheumatology (ACR) guidelines for the management of gout are a welcome addition to the resources at the disposal of practicing clinicians to assist in the management of this group of potentially complex patients4,5. The ACR guidelines recommend probenecid as an alternative first-line pharmacological urate-lowering therapy for those with a history of contraindication or intolerance to a xanthine oxidase inhibitor and with a creatinine clearance of 50 ml/min or more4. Probenecid is a uricosuric agent that lowers serum urate by inhibiting renal tubular reabsorption of uric acid. Probenecid is infrequently prescribed as a urate-lowering therapy in many parts of the world including the United States and Europe6,7. The study reported by Pui, et al in this issue of The Journal is of considerable interest8. Pui, et al report their real world experience of probenecid usage in 57 patients attending a rheumatology … Address correspondence to Dr. Conway, Department of Rheumatology, St. James Hospital, James Street, Dublin 8, Ireland; E-mail: drrichardconway{at}gmail.com
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".