Mortality Rates in Patients With Ankylosing Spondylitis With and Without Extraarticular Manifestations and Comorbidities: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To examine mortality rates in hospitalized patients with ankylosing spondylitis (AS) and the association of extraarticular manifestations (EAMs) and comorbidities with mortality rates. METHODS: This study was a retrospective, population-based cohort study using linked administrative data from patients with AS who were hospitalized (n = 1791) and patients in a matched comparison group (n = 8955). Mortality data for patients were obtained from the Western Australia Death Register. The presence of EAMs and comorbidities was identified from hospital records. Mortality rates were compared between the 2 groups using Cox proportional hazard models overall and stratified by a history of EAMs, comorbidities, and smoking status. RESULTS: Crude mortality rates were significantly higher among patients with AS than among patients in the comparison group (hazard ratio [HR] 1.85, 95% CI 1.62-2.12), with excess mortality in the AS group associated with cardiovascular disease (CVD; HR 5.32, 95% CI 3.84-7.35), cancer (HR 1.68, 95% CI 1.27-2.23), external causes (HR 3.92, 95% CI 2.28-6.77), and infectious diseases (HR 25.92, 95% CI 7.50-89.56). When patients were stratified by history of EAMs, CVD, and smoking, the risk of mortality was elevated in patients both with and without each risk factor. Among patients with AS, histories of CVD (HR 6.33, 95% CI 4.79-8.38), diabetes (HR 2.81, 95% CI 1.99-3.95), smoking (HR 1.49, 95% CI 1.18-1.89), and EAMs (HR 1.62, 95% CI 1.24-2.11) were associated with an increased risk of mortality. CONCLUSION: The presence of comorbidities, EAMs, and smoking contributes to an increased risk of all-cause mortality among patients with AS who are hospitalized compared to patients in the comparison group. These results support the need to prevent or reduce the occurrence of comorbidities and smoking in patients with AS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".