Do Monosodium Urate Crystals Reduce at Different Rates in Joints and Tendons During Urate-Lowering Therapy? A Dual-Energy Computed Tomography Study
Bibliographic record
Abstract
Objective Prior imaging studies have suggested that monosodium urate (MSU) crystal deposits in joints dissolve more rapidly than those in tendons during urate-lowering therapy (ULT) for gout. This study aimed to examine whether urate deposits visible on dual-energy computed tomography (DECT) reduce at different rates in joints and tendons during ULT. Methods Participants with gout from 2 clinical trials of oral ULT with the following criteria were included: paired DECT scans of the feet and ankles over 1-year of ULT, first DECT scan showing total urate volume ≥ 0.5 cm 3 , second DECT scan showing reduced total urate volume, and DECT deposition visible in at least 1 joint and 1 tendon on the first scan. DECT urate volumes in up to 3 index joints and up to 3 index tendons at baseline and year 1 were measured in known order. Data were analyzed using a general linear mixed analysis of covariance. Results In total, 125 joint deposits and 95 tendon deposits were analyzed from 50 participants. The least means change (95% CI) in DECT urate volumes of the joint deposits was −0.37 (−0.47 to −0.26) cm 3 and of the tendon deposits was −0.39 (−0.50 to −0.27) cm 3 (both P < 0.001). There was no difference in the change in DECT urate volumes between the joint and tendon deposits; least means difference was 0.02 (95% CI −0.09 to 0.13) cm 3 ( P = 0.73). Conclusion This DECT study indicates that similar rates of MSU crystal dissolution occur at both joints and tendons during oral ULT.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".