Venous Thromboembolism Prevention: A Systematic Review of Methods to Improve Prophylaxis and Decrease Events in the Hospitalized Patient
Bibliographic record
Abstract
Prevention of venous thromboembolism (VTE) is currently a key initiative internationally and in US hospitals, where there has been a recent focus on national quality initiatives to prevent hospital-acquired VTE. Multiple strategies exist to prevent VTE by increasing prophylaxis rates in the hospitalized setting. Active, multifaceted interventions, including provider education, an active reminder to the provider, and regular audit and feedback to medical and hospital staff, appear to be the most effective current interventions. Active intervention programs have been validated both as electronic alerts, with or without computerized clinical decision support software and, more recently, human alerts, many of which utilize in-hospital pharmacists. A passive strategy, such as guideline dissemination, should not be used as a lone method. Although inappropriate duration remains a key reason as to why at-risk patients do not receive appropriate thromboprophylaxis within the hospital (defined by type, dose, and duration of prophylaxis), few studies address duration compared with hospital length of stay. Preventable VTE is a new quality outcome measure for hospitals but is measured in few studies. Future studies should focus on comparing various multifaceted interventions to assess their effect over time, including endpoints of bleeding for safety, appropriate type, dose, and duration of prophylaxis, overall and preventable VTE, and the impact on unnecessary prophylaxis for patients not at risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".