Role of Diffusion-weighted and Contrast-enhanced Magnetic Resonance Imaging in Differentiating Activity of Ankylosing Spondylitis
Bibliographic record
Abstract
BACKGROUND: Previous studies showed that combining apparent diffusion coefficient (ADC) value with the Spondyloarthritis Research Consortium of Canada (SPARCC) index value might provide a reliable evaluation of the activity of ankylosing spondylitis (AS), and that contrast-enhanced (CE) magnetic resonance imaging (MRI) is unnecessary. However, the results were based on confirming only a small random sample. This study aimed to assess the role of CE-MRI in differentiating the disease activity of AS by comparing ADC value with a large sample. METHODS: A total of 115 patients with AS were enrolled in accordance with Bath AS Disease Activity Index and laboratory indices, and 115 patients were divided into two groups, including active group (n = 69) and inactive group (n = 46). SPARCC, ΔSI, and ADC values were obtained from the short tau inversion recovery (STIR), diffusion-weighted imaging (DWI), and CE-MRI, respectively. One-way analysis of variance and receiver operating characteristic analysis were performed for all parameters. RESULTS: The optimal cutoff values (with sensitivity, specificity, respective area under the curve, positive likelihood ratio, and negative likelihood ratio) for the differentiation between active and inactive groups are as follows: SPARCC = 6 (72.06%, 82.61%, 0.836, 4.14, 0.34); ΔSI (%) = 153 (80.6%, 84.78%, 0.819, 5.3, 0.23); ADC value = 1.15 × 10-3 mm2/s (72.73%, 81.82%, 0.786, 4, 0.33). No statistical differences were found among the predictive values of SPARCC, ΔSI, and ADC. Multivariate analysis showed no significant difference between the combination of SPARCC and ADC values with and without ΔSI. CONCLUSIONS: Using large sample, we concluded that the combination of STIR and DWI would play significant roles in assessing the disease activity, and CE-MRI sequence is not routinely used in imaging of AS to avoid renal fibrosis and aggravation of kidney disease.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".