Prevention of venous thromboembolism in hospitalized medical patients: addressing some practical questions
Bibliographic record
Abstract
PURPOSE OF REVIEW: To demonstrate that venous thromboembolism is an important and common problem in hospitalized medical patients; to outline anticoagulant prophylaxis management options and the evidence for efficacy and safety; and to identify medical patients in whom anticoagulant prophylaxis should be considered. RECENT FINDINGS: In at-risk medical patients who do not receive anticoagulant prophylaxis, the incidence of deep vein thrombosis is 10.5-14.9%. Prophylaxis with low-dose anticoagulant therapy confers a 57% reduction in the risk for symptomatic pulmonary embolism (relative risk, 0.43; 95% confidence interval: 0.26-0.71), a 62% reduction in the risk for fatal pulmonary embolism (relative risk, 0.38; 95% confidence interval: 0.21-0.69), and a 53% reduction in the risk for symptomatic deep vein thrombosis (relative risk, 0.47; 95% confidence interval: 0.22-1.00). Anticoagulant prophylaxis is also associated with a nonsignificant increased risk for major bleeding (relative risk, 1.32; 95% confidence interval: 0.73-2.37). Risk factors for venous thromboembolism and bleeding in medical patients have been proposed and may help identify patients in whom anticoagulant prophylaxis is indicated or contraindicated. However, validated risk-stratifications schemes are lacking. SUMMARY: Among hospitalized medical patients, randomized trials have established an acceptable therapeutic benefit-to-risk ratio of anticoagulant prophylaxis to reduce the incidence of clinically silent and symptomatic venous thromboembolism, including a reduction in the incidence of fatal pulmonary embolism. Additional research is needed to identify patients who would benefit most from anticoagulant prophylaxis.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".