American Society of Hematology 2019 guidelines for management of venous thromboembolism: prevention of venous thromboembolism in surgical hospitalized patients
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) is a common source of perioperative morbidity and mortality. OBJECTIVE: These evidence-based guidelines from the American Society of Hematology (ASH) intend to support decision making about preventing VTE in patients undergoing surgery. METHODS: ASH formed a multidisciplinary guideline panel balanced to minimize bias from conflicts of interest. The McMaster University GRADE Centre supported the guideline-development process, including performing systematic reviews. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess evidence and make recommendations, which were subject to public comment. RESULTS: The panel agreed on 30 recommendations, including for major surgery in general (n = 8), orthopedic surgery (n = 7), major general surgery (n = 3), major neurosurgical procedures (n = 2), urological surgery (n = 4), cardiac surgery and major vascular surgery (n = 2), major trauma (n = 2), and major gynecological surgery (n = 2). CONCLUSIONS: For patients undergoing major surgery in general, the panel made conditional recommendations for mechanical prophylaxis over no prophylaxis, for pneumatic compression prophylaxis over graduated compression stockings, and against inferior vena cava filters. In patients undergoing total hip or total knee arthroplasty, conditional recommendations included using either aspirin or anticoagulants, as well as for a direct oral anticoagulant over low-molecular-weight heparin (LMWH). For major general surgery, the panel suggested pharmacological prophylaxis over no prophylaxis, using LMWH or unfractionated heparin. For major neurosurgery, transurethral resection of the prostate, or radical prostatectomy, the panel suggested against pharmacological prophylaxis. For major trauma surgery or major gynecological surgery, the panel suggested pharmacological prophylaxis over no prophylaxis.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".