Clinical impact of bleeding in patients taking oral anticoagulant therapy for venous thromboembolism: a meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Clinicians should consider the clinical impact of anticoagulant-related bleeding when deciding on the duration of anticoagulant therapy in patients with venous thromboembolism. PURPOSE: To provide reliable estimates of the clinical impact of anticoagulant-related bleeding, defined as the case-fatality rate of major bleeding and the risk for intracranial bleeding. DATA SOURCES: MEDLINE (January 1989 to May 2003), Cochrane Controlled Trial Registry, thromboembolism experts, and reference lists; English-language literature only. STUDY SELECTION: Randomized, controlled trials and prospective cohort studies that investigated patients with venous thromboembolism who received oral anticoagulant therapy (target international normalized ratio, 2.0 to 3.0) for at least 3 months and that reported major bleeding and death as primary study outcomes. DATA EXTRACTION: Two reviewers independently extracted data on the number of anticoagulant-related major and intracranial bleeding episodes and on whether these events were fatal or nonfatal. DATA SYNTHESIS: The authors analyzed 33 studies involving 4374 patient-years of oral anticoagulant therapy. For all patients, the case-fatality rate of major bleeding was 13.4% (95% CI, 9.4% to 17.4%) and the rate of intracranial bleeding was 1.15 per 100 patient-years (CI, 1.14 to 1.16 per 100 patient-years). For patients who received anticoagulant therapy for more than 3 months, the case-fatality rate of major bleeding was 9.1% (CI, 2.5% to 21.7%), and the rate of intracranial bleeding was 0.65 per 100 patient-years (CI, 0.63 to 0.68 per 100 patient-years) after the initial 3 months of anticoagulation. CONCLUSION: The clinical impact of anticoagulant-related major bleeding in patients with venous thromboembolism is considerable, and clinicians should take this into account when deciding whether to continue long-term oral anticoagulant therapy in an individual patient.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.054 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".