Dual-Energy Computed Tomography Has Additional Prognostic Value Over Clinical Measures in Gout Including Tophi: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: This systematic literature review determined whether there is clinical utility for dual-energy computed tomography (DECT) to inform on prognosis for patients with gout. With DECT, individualized treatment plans could be developed based on the patient's unique urate burden, with DECT being used as a clinical outcome measure in gout management. METHODS: To evaluate DECT as a reliable, valid, and sensitive prognostic instrument, a librarian-assisted search was undertaken in PubMed and Embase for articles on gout and DECT informing on reliability; content, construct, and criterion validity; sensitivity to change; and minimum clinically important changes. RESULTS: This systematic literature review showed that DECT has high intra- and interrater reliability. Tophus burden correlates with functional loss to show content validity. DECT volume is positively correlated with death, cardiovascular risk factors, and the risk for future gout flares. DECT has excellent sensitivity to change with effective urate-lowering therapies. CONCLUSION: DECT is a promising prognostic tool based on its high reliability, sensitivity to change, and emerging validity. Additional large, well-designed, prospective cohort studies are needed to fully evaluate its prognostic utility. This systematic review suggests that DECT very likely has additional prognostic information beyond clinical tophi assessment alone.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".