Practice patterns for deep vein thrombosis prophylaxis in minimal-access surgery.
Bibliographic record
Abstract
BACKGROUND: There are no comprehensive evidence-based guidelines for deep vein thrombosis (DVT) prophylaxis in patients undergoing minimal-access surgery (MAS). METHODS: We completed a cross-sectional survey of general surgeons practising in Ontario, in order to establish current practice patterns for DVT prophylaxis for MAS procedures. RESULTS: The mean duration of practice of respondents was 15.4 years, with most (67.0%) practising outside an academic centre. For minor MAS, most surgeons do not give DVT prophylaxis (73.8% in laparoscopic cholecystectomy and 63.7% in laparoscopic inguinal hernia repair). For major MAS, a minority of surgeons do not give DVT prophylaxis (4.1% in laparoscopic colorectal surgery and 13.6% in laparoscopic splenectomy). However, there remains considerable variation in the mechanism (pharmacological, mechanical), approach and duration (perioperative, postoperative) of DVT prophylaxis among respondents in all case scenarios evaluated. Academic surgeons and surgeons in practice for 15 years or less are more aggressive with preoperative heparin administration. CONCLUSIONS: There is substantial and important variability in the current practice of general surgeons with respect to DVT prophylaxis for MAS. Considerable benefit will be derived from clinical trials that provide data to establish appropriate DVT prophylaxis guidelines for MAS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".