Correlation between clinical disease activity and sacroiliac magnetic resonance imaging detection in axial spondyloarthropathy
Bibliographic record
Abstract
Objectives: The study aimed to evaluate the correlation between the clinical disease activity of axial spondyloarthropathy (axSpA) and magnetic resonance imaging findings of the sacroiliac joint. Patients and methods: Thirty-two patients (21 males, 11 females; mean age: 39.3±9.2 years; range, 18 to 55 years) who were diagnosed with axSpA according to the Assessment in Spondyloarthritis International Society classification criteria between November 2015 and August 2017 were included in this cross-sectional study. Visual Analog Scale (VAS), Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Ankylosing Spondylitis Disease Activity Score (ASDAS)-erythrocyte sedimentation rate (ESR), and ASDAS-C-reactive protein (CRP) were used as the indicators of clinical activity. Magnetic resonance imaging of the sacroiliac joint was performed and the Spondyloarthritis Research Consortium of Canada (SPARCC) score was evaluated by a radiologist who was blinded to the clinical and laboratory parameters of the patients. Results: The mean duration of symptom onset was 9.3±7.7 years, and the mean duration of diagnosis was 3.6±2.8 years. Human leukocyte antigen (HLA)-B27 was positive in 16 (50%) patients. There was no correlation between the SPARCC score and VAS, BASDAI, MASES, BASFI, ASDAS-CRP, ASDAS-ESR, ESR, and CRP values (p>0.05). In the HLA-B27 subgroup analyses, a statistically significant correlation was found between HLA-B27-negative patients and SPARCC score (r=0.639, p=0.008). Conclusion: No relationship was found between other clinical disease parameters and sacroiliac joint imaging findings, except for the relationship between the SPARCC and BASDAI in HLA-B27- negative patients with axSpA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".