Venous thromboembolism rates in patients with bone and soft tissue sarcoma of the extremities following surgical resection: A systematic review
Bibliographic record
Abstract
BACKGROUND: Patients undergoing an orthopedic surgery for bone or soft tissue sarcoma are at increased venous thromboembolism (VTE) risk. Unfortunately, there is a lack of thromboprophylaxis guidelines in this population. The purpose of this systematic review was to determine the soft tissue and bone sarcoma VTE rate and to explore the thromboprophylaxis regimens used. METHODS: The databases MEDLINE, EMBASE, and CENTRAL were queried using keywords related to VTE and long bone malignancy requiring surgical intervention to 2020. Included studied reported VTE rate in patients with surgically managed extremity sarcoma. Descriptive statistics and weighted mean totals were calculated. RESULTS: A total of 2082 studies were screened and 23 studies were included. The overall VTE rate was 2.9%, with a rate of 3.7% and 1.4% in patients with bone and soft tissue sarcomas, respectively. Low-molecular-weight heparin was the most commonly used chemoprophylaxis. CONCLUSIONS: There is a high VTE rate following sarcoma surgery. The VTE rate is higher in bone sarcoma surgery, which may be attributed to differences in surgery and postoperative recovery. There was no consensus on the duration or type of thromboprophylaxis used. Future research is needed to determine the most effective thromboprophylaxis regimen in patients with sarcoma and whether individualized thromboprophylaxis is required.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".