A Systematic Review of Thromboprophylaxis for Pelvic and Acetabular Fractures
Bibliographic record
Abstract
OBJECTIVES: Pelvic and acetabular fractures have been identified as risk factors for deep venous thrombosis (DVT) and thromboembolic complications. A systematic review was performed to evaluate the effectiveness of thromboprophylactic strategies to prevent DVT or pulmonary embolism (PE) after pelvic or acetabular fractures. DATA SOURCES: Relevant articles were identified by searching MEDLINE, MEDLINE In Process & Other Non-indexed Citations, EMBASE, CENTRAL, and the Cochrane Database of Systematic Reviews. All languages and years indexed were searched. STUDY SELECTION: Manuscripts were included if (1) the study included an intervention or strategy aimed at preventing thromboembolic disease, (2) the subjects in the study had suffered a pelvic or acetabular fracture, and (3) the primary outcome of the study was DVT or PE. DATA EXTRACTION: The intervention, sample size, DVT, and/or PE incidence, and method of diagnosis were recorded for each study. DATA SYNTHESIS: Eleven studies with 1760 subjects were included. Included studies were grouped into 5 types of interventions: mechanical compression devices, inferior vena cava filters, low-molecular weight heparins, ultrasound screening, and magnetic resonance venography screening. Most studies were observational designs with minimal control data for comparison. Quantitative pooling was not possible based on significant study heterogeneity. CONCLUSIONS: Although several strategies have been used to prevent thromboembolism in pelvic and acetabular fracture patients, our results suggest that clinicians have limited data to guide their prophylactic decisions. Well-designed clinical trials to prevent and detect venous thromboembolism in pelvic and acetabular trauma are still needed.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".