Characteristics and Management of Patients with Venous Thromboembolism: The GARFIELD-VTE Registry
Bibliographic record
Abstract
BACKGROUND: Management of venous thromboembolism (VTE), encompassing both deep vein thrombosis (DVT) and pulmonary embolism (PE), varies worldwide. METHODS: The Global Anticoagulant Registry in the FIELD - Venous Thromboembolism (GARFIELD-VTE) is a prospective, observational study of 10,685 patients with objectively diagnosed VTE recruited from May 2014 to January 2017 at 417 sites in 28 countries. All patients are followed for at least 3 years. We describe the baseline characteristics of the study population and their management within 30 days of diagnosis. RESULTS: ). The most common risk factors were surgery (12.5%), hospitalization (12.0%) and trauma to the lower limbs (7.8%). At the time of VTE diagnosis, 10.1% had active cancer and 5.7% were chronically immobilized. Treatment for VTE was anticoagulant (AC) therapy alone in 90.9% of patients; 5.1% received thrombolytic and/or surgical/mechanical therapy ± AC and 4.0% received no therapy. Pre-diagnosis, 12.8% received AC therapy alone and 0.2% received thrombolytic and/or surgical/mechanical therapy ± AC. After diagnosis, parenteral AC therapy alone was administered in 17.6% of patients, and it was followed by a direct oral AC (DOAC) in 16.4% or a vitamin K antagonist (VKA) in 26.8%. DOACs alone were prescribed to 32.3% of patients, while 5.9% received VKA alone. CONCLUSION: The initial findings from this global registry highlight the heterogeneity in characteristics and management of VTE patients. Prospective follow-up will reveal the impact of this heterogeneity on outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".