Detection of the disease activity with ankylosing spondylitis through intravoxel incoherent motion diffusion-weighted MR imaging of sacroiliac joint
Bibliographic record
Abstract
Objective: To explore the value of the quantitative parameter of intravoxel incoherent motion diffusion (IVIM-DWI) at 3.0 T MRI of the sacroiliac joint in differentiating different disease activity statuses of ankylosing spondylitis (AS) and to compare it with traditional diffusion-weighted imaging (DWI) and Spondyloarthritis Research Consortium of Canada (SPARCC) score. Methods: 56 AS patients (active group, inactive group) and 24 healthy controls were included. Clinical data, quantitative parameters of IVIM-DWI MR images and the SPARCC scores were collected. The Kruskal–Wallis test was used to compare the differences between the groups. Receiver operating characteristic (ROC) curve analysis of histogram data and the SPARCC scores identified the efficacy of the three groups. The Spearman correlation coefficients were used to analyse the correlation between the quantitative IVIIM-DWI parameters and the SPARCC score. Results: The f (10th percentile) and SPARCC score of the active group were significantly higher than those of the inactive group. The f (10th, 25th, 50th percentiles), D slow (average, entropy, 10th ~ 90 th percentiles), D fast (kurtosis, skewness), ADC (average, 10th ~ 90 th percentiles) and the SPARCC score of the active group were significantly higher than the control group (p < 0.05). The AUC of the SPARCC score was the highest (0.799) in the identification between the active and inactive groups, and the sensitivity and specificity were 69.23 and 82.35%, respectively, at the cut-off value of 12. The SPARCC score was positively correlated with each percentile and the average value. Conclusions: Quantitative IVIIM-DWI parameters are helpful for the identification of different AS disease activity levels and are superior to traditional DWI. IVIM-DWI quantitative parameters had a good correlation with the SPARCC score. Advances in knowledge: A new MR technology-quantitative parameters of IVIM-DWI contribute to the identification of AS disease activity. IVIM-DWI quantitative parameters were well correlated with the SPARCC score.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".