Perioperative venous thromboembolism prophylaxis in prostate cancer surgery
Bibliographic record
Abstract
PURPOSE: To describe a patient and procedure specific approach to selecting Venous thromboembolism (VTE) prophylaxis for men who undergo radical prostatectomy. METHODS: We performed a literature search and narrative review of VTE after radical prostatectomy. We describe the current paradigm of perioperative thromboprophylaxis and underlying rationale. Relevant findings from the European Association of Urology thromboprophylaxis guidelines are interpreted and summarized. RESULTS: The use of extended post-operative thromboprophylaxis for patients who undergo radical prostatectomy is appropriate when the risk of symptomatic VTE outweighs the risk of major bleeding. Patient and procedure factors impact VTE risk. Patient risk can be stratified as low, moderate or high based on 4 factors; age > 75, BMI > 35, VTE in a first degree relative, and personal history of VTE. Procedure risk of VTE and bleeding can be stratified by modality of surgery (open, laparoscopic, robotic) and extent of pelvic lymphadenectomy. Using these factors, patients at the lowest risk for VTE will have an expected incidence of VTE of 0.4-0.8% and those at highest risk from 1.5 to 15.7%. Incidence of major bleeding ranges from 0.4 to 1.4%. These ranges emphasize the need to consider the net benefit for each specific patient. Use of mechanical prophylaxis is supported by weaker evidence but has fewer harms and is likely reasonable for most patients. CONCLUSION: Many patients who undergo radical prostatectomy will benefit from extended post-operative thromboprophylaxis. Risk of thrombosis is likely higher with open approach and extended lymph node dissection. The net benefit of treatment should be considered using patient- and procedure-specific criteria. When the net benefit is negligible or possibly harmful no pharmacological thromboprophylaxis should be used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".