Role of Dual-Energy Computed Tomography (DECT) in Detection of Carotid Artery Monosodium Urate Deposition (MSU) in Patients with CKD
Bibliographic record
Abstract
Background: To find out if a Dual energy CT can detect MSU deposition in carotid arteries in patients with CKD and whether the presence of monosodium urate crystals has any effect on atherosclerotic disease in terms of plaque volume. Methods: This is a retrospective study. All patients with CKD who underwent Dual energy neck imaging (like carotid angiogram, neck soft tissues or cervical spine) from January 2015 to December 2022 were included. Charts were reviewed to find CKD patients and potential confounding variables like isolated gout or smoking which can add to atherosclerotic disease. Same number of healthy controls were included which were then age (within 5 years), sex and confounders-matched to CKD patients using MedCalc. DECT datasets were post-processed using Syngo.ViaVB30 with MSU application and Calcium scoring application for volumetric analysis of atherosclerotic plaque. Results: Out of total 2157 patients who underwent dual energy neck imaging during study period, 85 were established CKD cases with confirmed clinical and laboratory evidence. Of 85 CKD cases, 2 were excluded due to presence of artefacts from dentures, n=83. Out of 83, MSU was detected in carotid arteries of 10 patients (12%). None of the matched control patient demonstrated MSU deposition. Volumetric analysis of atherosclerotic plaque demonstrated larger plaque volumes in CKD patients with MSU (n=10) than matched CKD patients without MSU (n=10) and matched control/non-CKD patients (n=10) (p value of 0.03). Conclusions: 1. Dual energy CT is an effective tool to detect MSU deposition in carotid arteries of patients with CKD. 2. MSU deposition increases atherosclerosis which can lead to increased TIA/strokes and hence has significant role in patient's morbidity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".