Knowledge of and Stated Adherence to the 2020 ACR Guideline for Gout Management: Results of a Survey of US Rheumatologists
Bibliographic record
Abstract
Objective This report evaluates rheumatologists' stated adherence to and agreement with the 2020 American College of Rheumatology (ACR) Guideline for the Management of Gout. Methods A 57-item questionnaire was administered to a sample of US rheumatologists. Stated adherence scores were based on several guideline recommendations reported to be followed by rheumatologists in practice, whereas stated agreement scores were based on whether respondents always followed the recommendations. Results All 201 rheumatologists approached completed the questionnaire. The mean overall stated adherence score was 11.5 (maximum 15), whereas the mean overall stated agreement score was 7.7 (maximum 14). Less experienced rheumatologists (≤ 8 yrs; n = 49) were likely to claim adherence to more individual ACR recommendations than those with more experience (> 8 yrs; n = 152; mean stated adherence score: 12.3 vs 11.3;P≤ 0.05). Rheumatologists who claimed to see ≤ 75 patients with gout in 6 months (n = 66) had a mean stated adherence score of 12.1 vs 11.2 for those who claimed to have seen > 75 patients (P≤ 0.05). Approximately 78% of rheumatologists claimed to follow the guideline for initiating urate-lowering therapy (ULT), and 89% were likely to prescribe allopurinol as a first-line ULT. Claimed adherence to recommendations for dosing was lower (febuxostat: 43%; allopurinol: 39%). Rheumatologists from academic settings were more likely to prescribe an interleukin-1 inhibitor for gout flares. Conclusion The self-reported practice of the surveyed US rheumatologists was generally concordant with the 2020 ACR Guideline for the Management of Gout. However, there were gaps in guideline knowledge and stated adherence among rheumatologists, mainly concerning the dosing of treatment regimens.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".