Treatment Target and Followup Measures for Patients with Gout: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the validity of serum uric acid (SUA) as a treatment target for patients with gout, and the clinimetric properties of the potential tools for monitoring these patients. METHODS: A search was performed in Medline, Embase and the Cochrane Library from inception to October 2011, and the 2010-2011 American College of Rheumatology and European League Against Rheumatism meeting abstracts. Studies evaluating different SUA levels or SUA reduction with the achievement of outcomes, and studies assessing clinimetric properties of instruments used to follow patients with gout were selected. Intervention studies were also included in order to estimate responsiveness. Titles and abstracts of the identified references were screened, and included articles were reviewed in detail and data collected using ad hoc standard forms. RESULTS: In total, 4575 articles were retrieved, 120 articles reviewed in detail, and 54 articles were included in the systematic literature review. SUA reduction was significantly associated with a reduction in acute attacks (6 studies), tophi regression (2 studies), and crystal clearance (3 studies). SUA 6.0 mg/dl was used as cutoff point in most of studies, but this level was found to be arbitrary. For followup of patients with gout, tophus measurement by caliper and ultrasound, the physical component of the Medical Outcomes Study Short Form-36 Survey, and Health Assessment Questionnaire have shown excellent clinimetric properties for this purpose. CONCLUSION: Reducing SUA is a valid treatment target for patients with gout, but the target level of reduction (cutoff point) is not clear. Some tools were found suitable for following patients with gout.
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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".