Atlantoaxial Subluxation in Patients With Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate the prevalence and incidence of atlantoaxial subluxation (AAS) in psoriatic arthritis (PsA), identify associated risk factors, and describe the clinical and radiographic characteristics of affected patients. METHODS: We included individuals from our observational cohort of PsA, excluding those with a history of trauma or cervical spine surgery. We calculated prevalence and incidence of AAS, and we used descriptive statistics to summarize and compare baseline demographic and disease-related characteristics between patients with and without AAS. Additionally, we used Cox regression with time-varying covariates to identify factors linked to AAS development and performed multivariable generalized estimating equations (GEE) analysis to assess associations with AAS. RESULTS: Among 1535 patients with PsA, 34 (2.21%) were identified with AAS, including 20 at baseline and 14 during follow-up, with an incidence of 1.16 per 1000 person-years. Patients with AAS had higher rates of radiographic sacroiliitis (64.71%) and greater peripheral joint damage. Elevated erythrocyte sedimentation rate (ESR) was observed in 84.85% of cases. Cox regression identified radiographic sacroiliitis (hazard ratio [HR] 6.61, 95% CI 1.64-26.67) as the strongest predictor of AAS, whereas male sex was associated with a lower hazard (HR 0.25, 95% CI 0.07-0.89). In GEE analysis, radiographic sacroiliitis (odds ratio [OR] 3.64, 95% CI 1.61-8.24) and higher modified Steinbrocker scores (OR 1.01, 95% CI 1.00-1.02) were associated with AAS, whereas older age (OR 0.95, 95% CI 0.92-0.98) and male sex (OR 0.40, 95% CI 0.17-0.92) were associated with lower ORs. CONCLUSION: AAS is an uncommon complication in PsA strongly associated with radiographic sacroiliitis and radiographic damage in peripheral joints.
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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 | 0.000 | 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".