Venous Thromboembolism Prevention in Rehabilitation
Bibliographic record
Abstract
ABSTRACT: Venous thromboembolism is a frequent complication of acute hospital care, and this extends to inpatient rehabilitation. The timely use of appropriate thromboprophylaxis in patients who are at risk is a strong, evidence-based patient safety priority that has reduced clinically important venous thromboembolism, associated mortality and costs of care. While there has been extensive research on optimal approaches to venous thromboembolism prophylaxis in acute care, there is a paucity of high-quality evidence specific to patients in the rehabilitation setting, and there are no clinical practice guidelines that make recommendations for (or against) thromboprophylaxis across the broad spectrum of rehabilitation patients. Herein, we provide an evidence-informed review of the topic with practice suggestions. We conducted a series of literature searches to assess the risks of venous thromboembolism and its prevention related to inpatient rehabilitation as well as in major rehabilitation subgroups. Mobilization alone does not eliminate the risk of venous thromboembolism after another thrombotic insult. Low molecular weight heparins and direct oral anticoagulants are the principal current modalities of thromboprophylaxis. Based on the literature, we make suggestions for venous thromboembolism prevention and include an approach for consideration by rehabilitation units that can be aligned with local practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".