Factors Influencing the Effectiveness of Allopurinol in Achieving and Sustaining Target Serum Urate in a US Veterans Affairs Gout Cohort
Bibliographic record
Abstract
OBJECTIVE: To assess factors associated with the ability to achieve and maintain target serum urate (SU) with allopurinol in patients with gout. METHODS: We used US Veterans Affairs (VA) databases from 2002-2012. Eligible patients had ≥ 1 inpatient or ≥ 2 outpatient visits with a diagnostic code for gout, filled a new index allopurinol prescription, had at least 1 posttreatment SU level measured, and met the 12-month observability rule. Treatment successes were defined as the achievement of postindex SU < 6 mg/dl (success 1) and postindex SU < 6 mg/dl that was sustained (success 2). RESULTS: Of the 198,839 unique patients with allopurinol use, 41,153 unique patients (with 47,072 episodes) and 17,402 unique patients (with 18,323 episodes) were eligible for analyses for success 1 and success 2; 42% each achieved (success 1) or achieved and maintained postindex SU < 6 mg/dl (success 2). In multivariable-adjusted models, factors associated with significantly higher odds of both outcomes were older age, normal body mass index, Deyo-Charlson index score of 0, rheumatologist as the main provider rather than non-rheumatologist, midwestern US location for the healthcare facility, a lower hospital bed size, military service connection for medical conditions of 50% or more (a measure of healthcare access priority), longer distance to the nearest VA facility, and lower preindex SU. CONCLUSION: We identified novel factors associated with maintaining SU < 6 mg/dl based on a theoretical model. Several potentially modifiable factors can be targeted by individual/provider/systems interventions for improving successful achievement and maintenance of target SU in patients with gout.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".