Clinical Impact of Multiparametric Contrast‐Enhanced Dual‐Energy Computed Tomography in Arthritis Imaging: A Prospective Single‐Center Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the influence of contrast-enhanced dual-energy computed tomography (CE-DECT) in detecting and differentiating rheumatic joint diseases of the hand. METHODS: In this prospective study, patients with suspected arthritis of the hand were investigated consecutively alongside the standard clinical procedure. CE-DECT with sequential rotations was performed in all patients before and 3-minutes after weight-adapted contrast agent application. Reconstructions included two-material decomposition for tophus imaging, virtual noncalcium for bone marrow edema, and CT subtraction for soft-tissue inflammation. All postprocessed images and original CT reconstructions were rated by two radiologists in consensus to generate an imaging diagnosis. Imaging findings in CE-DECT were juxtaposed with the initial and final evaluations by referring rheumatologists. This evaluation focused on surrogate performance criteria, emphasizing the added diagnostic value of CE-DECT in detecting specific imaging biomarkers associated with various arthritic pathologies. A subsequent survey assessed CE-DECT's diagnostic utility and impact on patient management, rated on a 1 to 10 scale. Descriptive statistics were employed. RESULTS: Overall, 136 patients were included in the analysis. In 119 patients (87.5%), the CE-DECT findings agreed with the final diagnosis. In 67 patients (49.2%), the diagnosis was changed following CE-DECT. Rheumatologists rated CE-DECT's diagnostic utility at a mean ± SD of 8.5 ± 2.1 and its contribution to patient management at 8.4 ± 1.8. CONCLUSION: CE-DECT showed high value for diagnosis and management of patients with suspected inflammatory arthritis. Its diverse diagnostic capabilities suggest that it can develop an important addition to current clinical workup pathways.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".