Intensity of spinal inflammation is associated with radiological structural damage in patients with active axial spondyloarthritis
Bibliographic record
Abstract
Abstract Objective The aim was to investigate the relationship between the intensity of spinal inflammation using the apparent diffusion coefficient (ADC) and radiographic progression in axial SpA. Methods This is a cross-sectional study of participants with axial SpA and back pain. Clinical, biochemical and radiological parameters were collected. The ankylosing spondylitis disease activity score (ASDAS)-CRP was determined. Radiographic progression was represented by the modified Stoke ankylosing spondylitis spine score (mSASSS). MRI with short tau inversion recovery (STIR) and diffusion-weighted imaging sequences were performed simultaneously. Inflammatory lesions on STIR were used for the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI indexes and as references in outlining regions of interest in ADC maps to produce mean (ADCmean) and maximal (ADCmax) ADC values. Univariate and multivariate linear regression analyses were used to determine independent associations between ADC and radiographic progression. Results The 84 participants with identifiable lesions on spinal ADC maps recruited were characterized by a mean (s.d.) age of 45.01 (13.68) years, long disease duration [13.40 (11.01) years] and moderate clinical disease activity [ASDAS-CRP 2.07 (0.83)]. Multivariate regression analysis using ADCmean as the independent variable showed that age (regression coefficient [B] = 0.34; P = 0.01), male sex (B = 0.25; P = 0.04) and ADCmean (B = 0.30; P = 0.01) were positively associated with mSASSS. Multivariate regression analysis using ADCmax as the independent variable showed a tendency for ADCmax to be associated with mSASSS (B = 0.21; P = 0.07). Conclusion The intensity of spinal inflammation as determined by ADC is associated with radiographic progression in participants with active axial SpA.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".