Role of Dual-Energy Computed Tomography in the Identification of Monosodium Urate Deposition in Gout Patients: A Comprehensive Analysis of 828 Joints According to Structural Joint Damage
Bibliographic record
Abstract
Background Dual-energy computed tomography (DECT) has become a promising, non-invasive procedure for the visualization, characterization, and quantification of monosodium urate (MSU) crystals, which aids clinicians in the diagnosis of gout. In this study, we aimed to examine the diagnostic accuracy of DECT in the evaluation of gout. Methodology This cross-sectional retrospective study included patients who were clinically diagnosed with gout and underwent a DECT scan. Results A majority (80.4%) of the MSU deposits were found in the ankle joints. The presence of MSU deposits on DECT scan was highly correlated with bone erosion in the upper limb (odds ratio [OR] = 132; 95% confidence interval [CI] = 17.3-1004.3), bone sclerosis in the lower limb (OR = 36.4; 95% CI = 15.4-86.1), bone erosion in metacarpophalangeal joints (OR = 160; 95% CI = 42.7-600.2), and bone sclerosis in metatarsophalangeal joints (OR = 35.6; 95% CI = 15.5-81.9). Using linear regression analysis on patient-level data, correlations were found between DECT MSU crystal deposition and damage on all categories of structural joint damage showing significant association with erosion (r = 0.91, p < 0.001) and space narrowing (r = 0.75, p < 0.001) but not with joints having periarticular calcification (r = 0.52, p < 0.041). Conclusions Our study established DECT as a valid method for detecting MSU deposits and their association with structural joint deterioration in a Vietnamese population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".