Prevention of venous thromboembolism: consensus, controversies, and challenges
Bibliographic record
Abstract
The last 50 years have witnessed a multitude of publications evaluating the efficacy, safety and cost effectiveness of many different thromboprophylaxis interventions. There is widespread consensus that thromboprophylaxis safely reduces morbidity and mortality. More than 25 evidence-based guidelines, published since 1986, also recommend routine thromboprophylaxis in the majority of hospitalized patients. As a result, thromboprophylaxis is recognized as a key safety priority for hospitals. Some of the remaining areas of controversy that will be discussed in this paper include the role of individual risk assessments to determine thrombosis risk and prophylaxis, replacement of low-dose heparin by low-molecular-weight heparin (LMWH), the optimal duration of prophylaxis, the role of combined thromboprophylaxis modalities, the safety of anticoagulant prophylaxis with regional analgesia, the use of LMWHs in chronic renal insufficiency, and the emerging role of new oral anticoagulants as thromboprophylactic agents. Despite the overwhelming evidence supporting thromboprophylaxis, rates of thromboprophylaxis use remain far from optimal. Successful implementation strategies to bridge this knowledge:care gap are the most important current challenges in this area. These strategies must be multifaceted, utilizing local, systems-based approaches as well as legislation and incentives that reinforce best practices.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".