A Pilot Study on Dual-Energy Computed Tomography for Detection of Urate Deposits in Renal Transplant Patients With Asymptomatic Hyperuricemia
Bibliographic record
Abstract
BACKGROUND: An increasing role of dual-energy computed tomography (DECT) scan in tophaceous gout assessment is recognized, whereas its role in asymptomatic hyperuricemia is unknown. OBJECTIVE: The objective of this study was to assess the prevalence of joint and renal monosodium urate deposits by DECT in asymptomatic hyperuricemia. METHODS: Among a renal transplant population with at least 1 year of follow-up, we included 27 patients with sustained hyperuricemia and 11 with normal serum uric acid (SUA) levels. We excluded patients with gout or history of monoarthritis or oligoarthritis. We registered demographic data, drugs, hyperuricemia onset, comorbidities, renal function, and SUA. We used a 128-slice dual-source CT system, and the acquisition protocol included the pelvis and imaging of elbows, wrists, hands, knees, ankles, and feet. The reading process was performed by 2 radiologists. RESULTS: The mean age was 43.7 ± 12 years, 57.8% were males, and median follow-up was 7 years. Hyperuricemia presented after a median time of 0.61 years after transplantation and had persisted for a median of 3.2 years (0.5-16.8 years). For the hyperuricemic group, the median SUA at the DECT scan and the maximum SUA levels were 7.9 and 8.9 mg/dL, respectively. Groups were similar in most of the clinical variables. We did not find any articular or renal deposit; conversely, we demonstrated a quadriceps tendon deposition in 1 patient with hyperuricemia (prevalence of 0.03%; 95% confidence interval, 0.006%-0.17%). CONCLUSIONS: In these patients with asymptomatic hyperuricemia, the prevalence of monosodium urate deposition assessed by DECT was low; however, larger studies need to be performed for further validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".