Major bleeding during secondary prevention of venous thromboembolism in patients who have completed anticoagulation: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The risk of major bleeding in patients who have completed anticoagulation therapy for unprovoked venous thromboembolism (VTE) is unknown. OBJECTIVE: To report the major bleeding and fatal bleeding rates in patients randomized to placebo or observation (i.e. no anticoagulation therapy) for the secondary prevention of recurrent VTE. PATIENTS AND METHODS: We performed a systematic review and meta-analysis of the literature to summarize the rates of major bleeding and fatal bleeding in patients randomized to placebo or observation during the secondary prevention of VTE. Unrestricted searches of MEDLINE (January 1, 1950 to August 31, 2013), Embase (January 1, 1980 to August 31, 2013), and the Cochrane Register of Controlled Trials using the OVID interface were conducted. Publications from potentially relevant journals were also searched by hand. We used a random-effects model to pool study results and I(2) testing to assess for heterogeneity. RESULTS: The analysis included 11 studies and 3965 patients who were followed for a median of 24 months. The overall pooled major bleeding rate was 0.45 per 100 patient-years (95% CI 0.29-0.64, I(2) = 0%), and the overall pooled fatal bleeding rate was 0.14 per 100 patient-years (95% CI 0.057-0.26, I(2) = 0%). CONCLUSIONS: Patients not receiving anticoagulant therapy for the secondary prevention of VTE experience major bleeding events, and this may have an impact on recommendations for extended treatment in this patient population.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".