Thromboembolic complications after surgical fixation of bone metastases: A systematic review
Bibliographic record
Abstract
BACKGROUND: Patients undergoing a major orthopedic surgery for metastatic bone disease (MBD) are at high risk of developing venous thromboembolic (VTE) complications. Despite concerns, there is no consensus on the most effective strategy to prevent VTE in these patients. The purpose of this systematic review was to determine the VTE rate following the surgical management of MBD. METHODS: The databases MEDLINE, EMBASE, and CENTRAL were searched using keywords related to VTE and MBD requiring surgical management. Included studies reported VTE rates in patients with surgically managed MBD. Descriptive statistics and weighted mean totals were calculated. RESULTS: In total, 2082 abstracts were screened, and 29 studies were included. The overall VTE rate was 4.7%. Patients receiving surgery for impending pathologic fracture had a higher rate of VTE (5.6%) compared to patients with acute pathologic fractures (4.2%). Low-molecular-weight heparin was the most used chemoprophylaxis. CONCLUSIONS: Relative to other cancer and orthopedic patients, the VTE rate is extremely high in patients with MBD. The discordant recommendations of thromboprophylaxis, and absence of research in this distinct and more granular surgical oncology subgroup, underpins the challenges associated with developing guidelines to lessen the VTE risks in the MBD patient population.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".