Dynamics of case-fatalilty rates of recurrent thromboembolism and major bleeding in patients treated for venous thromboembolism
Bibliographic record
Abstract
In patients with venous thromboembolism (VTE), assessment of the risk of fatal recurrent VTE and fatal bleeding during anticoagulation may help to guide intensity and duration of therapy. We aimed to provide estimates of the case-fatality rate (CFR) of recurrent VTE and major bleeding during anticoagulation in a 'real life' population, and to assess these outcomes according to the initial presentation of VTE and its etiology. The study included 41,826 patients with confirmed VTE from the RIETE registry who received different durations of anticoagulation (mean 7.8 ± 0.6 months). During 27,110 patient-years, the CFR was 12.1% (95% CI, 10.2-14.2) for recurrent VTE, and 19.7% (95% CI, 17.4-22.1) for major bleeding. During the first three months of anticoagulant therapy, the CFR of recurrent VTE was 16.1% (95% CI, 13.6-18.9), compared to 2.0% (95% CI, 0-4.2) beyond this period. The CFR of bleeding was 20.2% (95% CI, 17.5-23.1) during the first three months, compared to 18.2% (95% CI, 14.0-23.2) beyond this period. The CFR of recurrent VTE was higher in patients initially presenting with PE (18.5%; 95% CI, 15.3-22.1) than in those with DVT (6.3%; 95% CI, 4.5-8.6), and in patients with provoked VTE (16.3%; 95% CI, 13.6-19.4) than in those with unprovoked VTE (5.5%; 95% CI, 3.5-8.0). In conclusion, the CFR of recurrent VTE decreased over time during anticoagulation, while the CFR of major bleeding remained stable. The CFR of recurrent VTE was higher in patients initially presenting with PE and in those with provoked VTE.
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 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 | 0.002 | 0.023 |
| 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 | 0.001 | 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".