Baseline severity of sacroiliitis can predict acute inflammatory status of sacroiliac joint in early axial spondyloarthritis of male patients: a cross sectional study
Bibliographic record
Abstract
BACKGROUND: This study compared clinical, laboratory and radiographic features of axial spondyloarthritis (axSpA) between ankylosing spondylitis (AS) and non-radiographic axial spondyloarthritis (nrAxSpA) of young male patients. Additionally, we sought factors which can predict the baseline inflammatory status of sacroiliac joint (SIJ) in axSpA. METHODS: We retrospectively reviewed the medical records of 322 patients who visited our hospital due to inflammatory back pain, and 159 male patients with axSpA were enrolled. Enrolled patients were divided into two groups, AS group and nrAxSpA group, and medical records, laboratory data, radiologic findings were collected and analyzed. RESULTS: Alternating buttock pain and CRP elevation were significantly frequent in AS patients than nrAxSpA patients (68.8% vs 41.3%, P = 0.001, 63.5% vs 37.1%, P = 0.002), and SPondyloArthritis Research Consortium of Canada (SPARCC) score of SIJ was higher in AS patients than nrAxSpA patients (14.0 vs 5.0, P < 0.0001). Baseline sacroiliitis severity, psoriasis, and CRP elevation had positive association in univariate and multivariate regression analysis for SIJ inflammatory SPARCC score. CONCLUSION: AS patients were more frequently in acute inflammatory state than nrAxSpA patients according to laboratory and MRI finding. Baseline sacroiliitis grade was significantly associated with baseline inflammatory SPARCC score of SIJ. AS patients might need more intense initial treatment to resolve active inflammatory lesion of SIJ and prevent further radiologic progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".