Factors Predictive of Radiographic Progression in Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Using a longitudinal observational cohort of ankylosing spondylitis (AS) patients, we sought to identify progression rates and factors predictive of spinal progression. As a secondary aim, we analyzed the effect of tumor necrosis factor inhibitor (TNFi) treatment on radiographic progression. METHODS: AS patients who had baseline and follow-up cervical and lumbar radiographs were included in the study. Radiographic damage was assessed by the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS). A change of 2 mSASSS units in 2 years was defined as progression. The characteristics of the study group such as demographic, clinical, laboratory, and treatment history were collected. RESULTS: There were 350 patients in the study. The mean ± SD mSASSS increased from 9.3 ± 15.8 units at baseline to 17.7 ± 21.7 units by the sixth year. Mean ± SD changes in mSASSS between the years 0 to 2, 2 to 4, and 4 to 6 were 1.23 ± 2.68, 1.47 ± 2.86, and 1.52 ± 3.7 units, respectively. Overall, 24.3% of the group progressed over 2 years. Male sex (hazard ratio [HR] 2.46 [95% confidence interval (95% CI) 1.05, 5.76]), the presence of baseline damage (HR 7.98 [95% CI 3.98, 16]), increased inflammatory markers (log C-reactive protein level HR 1.35 [95% CI 1.07, 1.70]), and TNFi use (HR 0.82 [95% CI 0.70, 0.96]) were predictive of radiographic progression. There was a 20% reduction in the rate of progression with TNFi. CONCLUSION: Male sex, the presence of baseline damage, active disease state, and higher inflammatory markers confer a high risk for disease progression. Treatment with TNFi showed a disease-modifying effect by slowing the rate of radiographic 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".