The risk of venous thromboembolism in ankylosing spondylitis : a general population-based study
Bibliographic record
Abstract
Background: Venous thromboembolism (VTE) including pulmonary embolism (PE) and deep vein thrombosis (DVT) can be life threatening. An increased frequency of VTE has been found in inflammatory conditions. To date, evidence assessing whether this risk is also greater in ankylosing spondylitis (AS) patients is scarce. Methods: Using the provincial British Columbia, Canada healthcare database that encompasses all residents within the province, we conducted matched cohort analyses of incident PE, DVT, and overall VTE amongst incident cases of AS and compared them with individuals randomly selected from the general population without AS. We calculated incidence rates of VTE and multivariable analyses after adjusting for traditional risk factors using Cox models. Results: Among 7,190 incident cases of AS, 35 developed PE and 47 developed DVT. Incidence rates (IR) of PE, DVT, and overall VTE per 1,000 person-years for AS patients were 0.79, 1.06, 1.56 compared with 0.40, 0.50, 0.77 in the control cohort. Corresponding fully adjusted HRs (95% CI) of PE, DVT, and VTE were 1.36 (0.92 to 1.99), 1.62 (1.16 to 2.26), and 1.53 (1.16 to 2.01). The risks of PE, DVT, and VTE were highest in the first year of diagnosis with HR (95% CI) of 2.88 (0.87 to 9.62), 2.20 (0.80 to 6.03), and 2.10 (0.88 to 4.99). Conclusions: These findings demonstrate an increased risk of VTE in the general AS population. This risk appears the most prominent in the first year after diagnosis.
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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 | 0.002 |
| 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".