Bibliographic record
Abstract
Venous thromboembolism is the most common preventable cause of death in surgical patients. Thromboprophylaxis, using mechanical methods to promote venous outflow from the legs and antithrombotic drugs, provides the most effective means of reducing morbidity and mortality in these patients. Despite the evidence supporting thromboprophylaxis, it remains underused because surgeons perceive that the risk of venous thromboembolism is not high enough to justify the potential hemorrhagic complications of anticoagulant use. The risk of venous thromboembolism is determined by patient characteristics and by the type of surgery that is performed. In this paper we identify the risk factors for venous thromboembolism and provide a scheme for stratifying surgical patients according to their risk. We describe the mechanism of action of the various forms of thromboprophylaxis and outline the evidence supporting thromboprophylaxis in different surgical settings. Finally, we recommend optimal forms of thromboprophylaxis in patients who undergo various types of surgery. Intermittent pneumatic compression, with or without elastic stockings, can be used for thromboprophylaxis in patients who undergo neurosurgical procedures; for patients who undergo vascular or cardiovascular procedures, long-term acetylsalicylic acid should be used for thromboprophylaxis. Low-molecular-weight heparin (LMWH) or warfarin is the choice for patients with spinal cord operations and all patients with major trauma who do not have contraindications to anticoagulation should receive thromboprophylaxis with LMWH.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".