Why Do Some Patients Have Severe Sacroiliac Disease But No Syndesmophytes in Ankylosing Spondylitis? Data From a Nested Case-Control Study
Bibliographic record
Abstract
Objective Sacroiliac (SI) joint and spinal inflammation are characteristic of ankylosing spondylitis (AS), but some patients with AS have been identified who have discordant radiographic disease. We studied an AS subgroup with long-standing disease and fused SI joints. We identified factors associated with discrepant degrees of radiographic damage between the SI joints and spine. Methods From the Prospective Study of Outcomes in AS (PSOAS) cohort, patients with a disease duration ≥ 20 years and fused SI joints were included in a nested case-control design. Patients with and without syndesmophytes were used as cases and controls for analysis. We used classification and regression tree (CART) analysis to determine risk factors for syndesmophytes presence and reexamined the validity of the risk factors using univariable logistic regression models. Results There were 354 patients in the subgroup, 23 of whom lacked syndesmophytes. CART analysis showed females were less likely to have syndesmophytes. The next important predictor was age of symptom onset in males, with age of onset ≤ 16 years being less likely to have syndesmophytes. Univariable analysis confirmed females were less likely to have syndesmophytes (odds ratio [OR] 0.17, 95% CI 0.07-0.41). Syndesmophyte presence was associated with HLA-B27 positivity (P= 0.03) and age of symptom onset > 16 years old (OR 2.72, 95% CI 1.15-6.45). All 23 patients who lacked syndesmophytes were HLA-B27 positive. Conclusion Using CART analysis and univariable modeling, women were less likely to have syndesmophytes despite advanced disease duration and SI joint disease. Patients with younger age of symptom onset were less likely to have syndesmophytes. All patients without syndesmophytes were HLA-B27 positive, indicating HLA-B27 positivity may be more associated with SI disease than spinal disease.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".