Thromboprophylaxis practice patterns and beliefs among physicians treating patients with abdominopelvic cancers at a Canadian centre
Bibliographic record
Abstract
Background: There is inadequate high-quality evidence on thromboprophylaxis for patients undergoing surgery for abdominopelvic cancer. We surveyed physicians who treat patients with abdominopelvic cancer to determine current thromboprophylaxis practice patterns and to determine where research is needed. Methods: We created an online survey with questions on thromboprophylaxis topics, including type of thromboprophylaxis used, timing of initial thromboprophylaxis dose, use of thromboprophylaxis during chemotherapy, use of extended-duration thromboprophylaxis and areas for future research. The survey questions were reviewed by external content experts to ensure they were appropriate and relevant. Surgeons, thrombosis experts and medical oncologists who manage patients with abdominopelvic cancers at 1 large Canadian academic centre were invited to complete the survey between January and April 2019. Results: Of the 57 physicians invited, 42 (74%) completed the survey, including 27 surgeons (response rate 79%), 9 thrombosis experts (response rate 75%) and 6 medical oncologists (response rate 55%). Most surgeons (22 [82%]) reported using mechanical thromboprophylaxis, whereas only 1 thrombosis expert (11%) recommended mechanical thromboprophylaxis. There was substantial variability in the timing of the initial dose of thromboprophylaxis, with 9/10 urologists (90%) and all 7 general surgeons giving the first dose intraoperatively, and three-quarters of thoracic surgeons (3/4 [75%]), gynecologists (3/4 [75%]) and thrombosis experts (7/9 [78%]) starting thromboprophylaxis after surgery. All medical oncologists believed chemotherapy increases the risk of venous thromboembolism, but 4 (67%) reported that they do not routinely prescribe thromboprophylaxis owing to bleeding concerns. Most respondents (35/38 [92%]) felt there was a need for more research on thromboprophylaxis and indicated willingness to participate in future clinical trials. Conclusion: Variability exists in contemporary thromboprophylaxis practice patterns among physicians treating patients with abdominopelvic cancer. Future research is needed to standardize care and improve outcomes for patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".