Multinational evidence-based recommendations for the diagnosis and management of gout: integrating systematic literature review and expert opinion of a broad panel of rheumatologists in the 3e initiative
Bibliographic record
Abstract
We aimed to develop evidence-based multinational recommendations for the diagnosis and management of gout. Using a formal voting process, a panel of 78 international rheumatologists developed 10 key clinical questions pertinent to the diagnosis and management of gout. Each question was investigated with a systematic literature review. Medline, Embase, Cochrane CENTRAL and abstracts from 2010-2011 European League Against Rheumatism and American College of Rheumatology meetings were searched in each review. Relevant studies were independently reviewed by two individuals for data extraction and synthesis and risk of bias assessment. Using this evidence, rheumatologists from 14 countries (Europe, South America and Australasia) developed national recommendations. After rounds of discussion and voting, multinational recommendations were formulated. Each recommendation was graded according to the level of evidence. Agreement and potential impact on clinical practice were assessed. Combining evidence and clinical expertise, 10 recommendations were produced. One recommendation referred to the diagnosis of gout, two referred to cardiovascular and renal comorbidities, six focused on different aspects of the management of gout (including drug treatment and monitoring), and the last recommendation referred to the management of asymptomatic hyperuricaemia. The level of agreement with the recommendations ranged from 8.1 to 9.2 (mean 8.7) on a 1-10 scale, with 10 representing full agreement. Ten recommendations on the diagnosis and management of gout were established. They are evidence-based and supported by a large panel of rheumatologists from 14 countries, enhancing their utility in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.340 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.049 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".