Vertebral Erosions Associated with Spinal Inflammation in Patients with Ankylosing Spondylitis Identified by Magnetic Resonance Imaging: Changes After 2 Years of Tumor Necrosis Factor Inhibitor Therapy
Bibliographic record
Abstract
OBJECTIVE: Spinal inflammation and erosions have been described in magnetic resonance imaging (MRI) examinations of patients with ankylosing spondylitis (AS). MRI scoring systems have implemented these observations. METHODS: MRI scans (T1 or short-tau inversion recovery) from tumor necrosis factor-α blocker (anti-TNF) trials with patients with active AS (n = 22) were analyzed at baseline and after 2 years based on vertebral units (VU). The analysis was based on the prevalence of spinal erosions in relation to inflammation (active erosions) or without it (inactive erosions) as an outcome measure on MRI and their course under anti-TNF therapy. The results of MRI scoring systems that include (ASspiMRI) or exclude (Berlin score) erosions were also compared. RESULTS: At baseline, there were more VU with inflammation (33.7%) than with erosions irrespective of activity (10.6%). After 2 years, active erosions decreased to 3.7% while inflammation was seen in a total of 12% of VU - a reduction of 58.9% and 64.5%, respectively (both p < 0.02). The overall extent of erosions decreased from 10.6% at baseline to 5.6% at 2 years. At the patient level, 73% and 32% of patients showed active erosions (p = 0.002), while 100% and 64% of patients showed inflammation (p = 0.029) at baseline and 2 years, respectively. Both scoring systems showed similar improvement, independent of inclusion or exclusion of erosions. CONCLUSION: Inflammation with erosions was observed in the spine of most patients with AS but their contribution to changes observed upon anti-TNF therapy was small, indicating that erosions do not need to be included in quantitative scoring systems of inflammation. Spinal inflammation was still present after 2 years of anti-TNF therapy in two-thirds of patients.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".