European guidelines on perioperative venous thromboembolism prophylaxis
Bibliographic record
Abstract
: Although there are numerous publications addressing venous thromboembolism and its prevention in neurosurgery, there are relatively few high-quality studies to guide decisions regarding thromboprophylaxis. In patients undergoing craniotomy, we recommend that if intermittent pneumatic compression (IPC) is used, it should be applied before the surgical procedure or on admission (Grade 1C). In craniotomy patients at particularly high risk for venous thromboembolism, we suggest considering the initiation of mechanical thromboprophylaxis with IPC preoperatively with addition of low molecular weight heparin (LMWH) postoperatively when the risk of bleeding is presumed to be decreased (Grade 2C). In patients with non-traumatic intracranial haemorrhage, we suggest thromboprophylaxis with IPC (Grade 2C). For patients who have had non-traumatic intracranial haemorrhage, we suggest giving consideration to commencement of LMWH or low-dose unfractionated heparin when the risk of bleeding is presumed to be low (Grade 2C). We suggest continuing thromboprophylaxis until full mobilisation of the patient (Grade 2C). For patients undergoing spinal surgery with no additional risk factors, we suggest no active thromboprophylaxis intervention apart from early mobilisation (Grade 2C). For patients undergoing spinal surgery with additional risk factors, we recommend starting mechanical thromboprophylaxis with IPC (Grade 1C), and we suggest the addition of LMWH postoperatively when the risk of bleeding is presumed to be decreased (Grade 2C).
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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 | 0.005 | 0.012 |
| 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 | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".