Frequency of Allopurinol Dose Reduction in Hospitalized Patients With Gout Flares
Bibliographic record
Abstract
To the Editor: The risk of subsequent flares after the initial diagnosis of gout remains high, according to the recent study, “Changes in the Presentation of Incident Gout and the Risk of Subsequent Flares: A Population-based Study Over 20 Years” by Elfishawi, et al 1. This study found 60% of the patients have at least 1 subsequent flare episode within 5 years of their initial gout diagnosis. Despite an improved understanding of gout pathophysiology and treatment options, the prevalence of subsequent flares in the 2009–2010 cohort has not significantly improved compared to the 1989–1992 cohort. One of the identified risk factors was the persistently elevated serum uric acid (SUA). This finding highlights the importance of adequately treating gout to their target SUA. Limitations in their dataset may have precluded the authors from studying urate-lowering therapy (ULT) changes or discontinuation as a risk factor for subsequent flare. We report the results of our study below, in which we evaluated the frequency of inpatient adjustment of ULT in hospitalized … Address correspondence to Dr. I.J. Huang, 1959 NE Pacific St., BB561, Seattle, WA 98195, USA. Email: ijhuang{at}uw.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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".