The use of low molecular weight heparins for the prevention of postoperative venous thromboembolism in general surgery. A survey of practice in the United States.
Bibliographic record
Abstract
BACKGROUND: Even though low molecular weight heparins (LMWHs) have become the standard for venous thromboembolism (VTE) prophylaxis in most European countries and Canada, it was not until recently that LMWHs were approved for use in the United States. The main objective of this study was to assess the current preferences and attitudes of United States surgeons toward the prevention of VTE with particular reference to LMWH. METHODS: A survey with questions relative to VTE awareness, risk factors, and prevention practices was mailed to 10,000 Fellows of the American College of Surgeons. RESULTS: A total of 1,145 (11.45%) usable questionnaires were returned. The vast majority (96%) of respondents use prophylaxis against VTE. Although LMWHs were rated first regarding efficacy and second regarding simplicity of use, conventional unfractionated heparin at fixed doses remains the preferred pharmacological agent for VTE prevention (74%), followed by 2 LMWHs: enoxaparin (34%) and dalteparin (16%). Overall, 52% of surgeons preferred physical methods over pharmacological methods when used separately and 26% of surgeons utilize combined physical-pharmacological modalities. CONCLUSIONS: North American general surgeons have substantially modified their approach to VTE prevention in the last 4 years. Physical methods and unfractionated heparin remain the preferred prophylactic modalities, but LMWHs have gained rapid acceptance since their approval for use for VTE prevention in North America. Even though the results of this survey must be interpreted with caution because of the limited response rate and possible sampling bias, they still reflect the current preferences and attitudes of North American surgeons toward prophylaxis.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".