European guidelines on perioperative venous thromboembolism prophylaxis
Bibliographic record
Abstract
: None of the predictive models for venous thromboembolism (VTE) prophylaxis have been designed for and validated in patients undergoing cardiothoracic and vascular surgery. The presence of one or more risk factors [age over 70 years old, transfusion of more than 4 U of red blood cells/fresh frozen plasma/cryoprecipitate, mechanical ventilation lasting more than 24 h, postoperative complication (e.g. acute kidney injury, infection/sepsis, neurological complication)] should place the cardiac population at high risk for VTE. In this context, we suggest the use of pharmacological prophylaxis as soon as satisfactory haemostasis has been achieved, in addition to intermittent pneumatic compression (IPC) (Grade 2C). In patients undergoing abdominal aortic aneurysm repair, particularly when an open surgical approach is used, the risk for VTE is high and the bleeding risk is high. In this context, we suggest the use of pharmacological prophylaxis as soon as satisfactory haemostasis is achieved (Grade 2C). Patients undergoing thoracic surgery in the absence of cancer could be considered at low risk for VTE. Patients undergoing thoracic surgery with a diagnosis of primary or metastatic cancer should be considered at high risk for VTE. In low-risk patients, we suggest the use of mechanical prophylaxis using IPC (Grade 2C). In high-risk patients, we suggest the use of pharmacological prophylaxis in addition to IPC (Grade 2B).
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".