The Discriminative Values of the Bath Ankylosing Spondylitis Disease Activity Index, Ankylosing Spondylitis Disease Activity Score, C-Reactive Protein, and Erythrocyte Sedimentation Rate in Spondyloarthritis-Related Axial Arthritis
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to determine the effectiveness of Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Ankylosing Spondylitis Disease Activity Score-C-Reactive Protein (ASDAS-CRP), Ankylosing Spondylitis Disease Activity Score-Erythrocyte Sedimentation Rate (ASDAS-ESR), and inflammatory markers in screening for axial-joint inflammation as detected by magnetic resonance imaging (MRI) and to find out factors that could affect scoring of the indices. METHODS: One hundred fifty-three Chinese spondyloarthritis patients were recruited. Clinical data and BASDAI were collected, and Bath Ankylosing Spondylitis Metrology Index was measured. Serum ESR and CRP were checked, and ASDAS-ESR and ASDAS-CRP were calculated. Radiographs of cervical and lumbar spine were performed for modified Stoke Ankylosing Spondylitis Spinal Score. All patients underwent MRI of the spine and sacroiliac joints. Axial-joint inflammation was evaluated by Spondyloarthritis Research Consortium of Canada MRI indices. Multivariate linear regressions were used to determine potential factors that could affect disease activity indices. Receiver operating characteristic curve was used to determine the effectiveness in screening for axial-joint inflammation. RESULTS: BASDAI was associated with current back pain (B = 0.89, P = 0.01), ASDAS-CRP with current back pain (B = 0.74, P = 0.04), and current dactylitis (B = 0.70, P = 0.03) ASDAS-ESR with current back pain (B = 0.95, P = 0.01), and current dactylitis (B = 0.99, 0.002). The ROC curve revealed that CRP was the only variable that successfully discriminated spondyloarthritis patients with and without axial-joint inflammation by MRI, although it had poor accuracy (area under the curve, 0.63; 95% confident interval, 0.53-0.72; P = 0.01). CONCLUSIONS: Based on our results, MRI could be used to supplement traditional disease assessment tools for more accurate disease evaluation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".