Risk of Pulmonary Embolism and Deep Venous Thrombosis in Systemic Sclerosis: A General Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: To determine the risk of venous thromboembolism (VTE) (pulmonary embolism [PE] and deep vein thrombosis [DVT]) in individuals with incident systemic sclerosis (SSc; scleroderma) in the general population. METHODS: Using a population database that includes all residents of British Columbia, Canada, we conducted a cohort study of all patients with incident SSc and up to 10 age-, sex-, and entry time-matched individuals from the general population. We compared incidence rates of PE, DVT, and VTE between the 2 groups according to SSc disease duration. We calculated hazard ratios (HRs), adjusting for confounders. RESULTS: Among 1,245 individuals with SSc (83% female, mean age 56 years), the incidence rates of PE, DVT, and VTE were 3.47, 3.48, and 6.56 per 1,000 person-years, respectively, whereas the corresponding rates were 0.78, 0.76, and 1.37 per 1,000 person-years among 12,670 non-SSc individuals. Compared with non-SSc individuals, the multivariable HRs among SSc patients were 3.73 (95% confidence interval [95% CI] 1.98-7.04), 2.96 (95% CI 1.54-5.69), and 3.47 (95% CI 2.14-5.64) for PE, DVT, and VTE, respectively. The age-, sex-, and entry time-matched HRs for PE, DVT, and VTE were highest during the first year after SSc diagnosis (32.77 [95% CI 6.60-162.75], 8.50 [95% CI 3.13-23.04], and 12.03 [95% CI 5.27-27.45], respectively). CONCLUSION: These findings provide population-based evidence that SSc patients are at a substantially increased risk of VTE, especially within the first year after SSc diagnosis. Increased monitoring for this potentially fatal outcome and its modifiable risk factors is warranted in this patient population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".