Predictors of Success in Gout Treatment
Bibliographic record
Abstract
In this issue of The Journal , Singh, et al , from the University of Alabama, asked the key question regarding outcomes for people with gout: what are the factors associated with achieving and maintaining target serum urate (SU) concentrations with allopurinol1? To examine this, they have accessed a large, longitudinal cohort of patients with gout in the US Veterans Administration (VA) system from 2002 to 2012. To be included in the study, a patient needed a diagnostic code of gout for ≥ 1 inpatient episode or ≥ 2 outpatient visits, a new prescription for allopurinol, and a record in the VA system for at least 12 months. A successful outcome was achieving a target SU concentration of < 6 mg/dl (0.36 mmol/l) 14 days or more after the index allopurinol treatment. Successful maintenance was defined as those whose SU remained < 6 mg/dl at all subsequent measurements. There were 627,693 patients with gout in the VA system and 198,839 patients had a new prescription of allopurinol. However, only 41,153 had at least 1 SU result recorded and only 42% of these reached the target SU. This took a mean of 9 months to achieve. Only 17,402 incident allopurinol users had 2 or more SU results and of these, 42% achieved and maintained SU < 6 mg/dl for all blood samples tested over the period of observation. These findings are not surprising from what we know about outcomes for people with gout. However, they are depressing given that this form of arthritis can be controlled very well, if managed appropriately, in almost all … Address correspondence to Dr. R.O. Day, Department of Clinical Pharmacology and Toxicology, St. Vincent’s Hospital, Darlinghurst, Sydney 2010, Australia. E-mail: r.day{at}unsw.edu.au
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".