Thromboprophylaxis in medical patients: the role of low-molecular-weight heparin
Bibliographic record
Abstract
Many hospitalised medical patients are at increased risk of venous thromboembolism (VTE). Consensus statements recommend that such patients be assessed for risk of VTE on admission to hospital and receive thromboprophylaxis where appropriate. However, VTE prophylaxis is not widely used in medical patients. One explanation is that assessing medical patients' risk of VTE is complicated. The risk depends not only on the current illness but also on multiple intrinsic factors, and a variety of strategies for identifying patients who should receive thromboprophylaxis have been suggested. Thromboprophylaxis with unfractionated heparin (UFH) has proved to be effective in reducing the incidence of deep-vein thrombosis and overall mortality in medical patients. Clinical trial evidence, including a meta-analysis, suggests that thromboprophylaxis with low-molecular-weight heparin (LMWH) is at least as effective as with UFH, and also has the advantage of fewer bleeding complications. In particular, two large, randomised clinical trials--Prophylaxis in Medical Patients with Enoxaparin (MEDENOX) and Prospective Evaluation of Dalteparin Efficacy for Prevention of VTE in Immobilized Patients Trial (PREVENT)--showed that thromboprophylaxis with the LMWHs enoxaparin (40 mg s.c. once daily) or dalteparin (5,000 IU once daily) is more effective than placebo and well tolerated in medical patients. In addition, the Thromboembolism-Prevention in Cardiopulmonary Diseases with Enoxaparin (THE-PRINCE) trial showed that enoxaparin treatment was as effective as UFH. These studies provide solid evidence for the widespread use of thromboprophylaxis in medical patients.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".