Time Trends and Excess Mortality Compared to Population Controls after a First-Time Pulmonary Embolism or Deep Vein Thrombosis
Bibliographic record
Abstract
Abstract Recent data on temporal trends in excess mortality for patients with pulmonary embolism (PE) and deep vein thrombosis (DVT) compared with the general population are scarce. A nationwide Swedish register study conducted from 2006 to 2018 including 68,960 PE and 70,949 DVT cases matched with population controls. Poisson regression determined relative risk (RR) for 30-day and 1-year mortality trends while Cox regression determined adjusted hazard ratios (aHRs). A significance level of 0.001 was applied. In PE cases, both 30-day mortality (12.5% in 2006 to 7.8% in 2018, RR: 0.95 [95% CI: 0.95–0.96], p < 0.0001) and 1-year mortality (26.5 to 22.1%, RR: 0.98 [0.97–0.98], p < 0.0001) decreased during the study period. Compared with controls, no significant change was seen in 30-day (aHR: 33.08 [95% CI: 25.12–43.55] to 24.64 [95% CI: 18.81–32.27], p = 0.0015 for interaction with calendar year) or 1-year (aHR: 5.85 [95% CI: 5.31–6.45] to 7.07 [95% CI: 6.43–7.78], p = 0.038) excess mortality. The 30-day excess mortality decreased significantly (aHR: 39.93 [95% CI: 28.47–56.00) to 24.63 [95% CI: 17.94–33.83], p = 0.0009) in patients with PE without known cancer before baseline, while the excess 1-year mortality increased (aHR: 3.55 [95% CI: 3.16–3.99] to 5.38 [95% CI: 4.85–5.98], p < 0.0001) in PE cases surviving to fill a prescription of anticoagulation. In DVT cases, 30-day and 1-year mortality declined, while excess mortality compared with controls remained stable. In general, the improved mortality following PE and DVT paralleled population trends. However, PE cases without cancer had decreasing excess 30-day mortality, whereas those surviving to fill a prescription for anticoagulant medication showed increasing excess 1-year mortality.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".