Extended-duration thromboprophylaxis following major abdominopelvic surgery – For everyone or selected cases only?
Bibliographic record
Abstract
Major abdominopelvic surgery is an important risk factor for postoperative venous thromboembolism (VTE). VTE is the leading cause of 30-day postoperative mortality in patients with cancer undergoing major abdominopelvic surgery. Randomized controlled trials have shown that extended duration thromboprophylaxis using a low molecular weight heparin or a direct oral anticoagulant significantly decreases the risk of overall VTE (symptomatic events and asymptomatic deep vein thrombosis). Hence, several clinical practice guidelines suggest the use of extended duration thromboprophylaxis for all high-risk patients undergoing major abdominopelvic surgery. Despite these recommendations by clinical practice guidelines, adoption of extended duration thromboprophylaxis in clinical practice remains low and clinical equipoise seems to persist. In this narrative review, we aim is to highlight and summarize the reasons that may explain discrepancy between clinical guideline recommendations and current practice regarding extended duration thromboprophylaxis in this patient population. We also aim to review different personalized approaches based on patients' individualized risk of VTE that may foster shared decision making and improve patient outcomes by reducing decisional conflict, increasing patient knowledge, and increasing risk perception accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".