Thromboembolic Risk After Total Hip Replacement Versus Hemiarthroplasty in Femoral Neck Fracture Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: Femoral neck fractures are common among elderly patients and are typically managed surgically to restore mobility and reduce complications. Total Hip Replacement (THR) and Hemiarthroplasty (HA) are standard interventions. While both procedures are widely used, the comparative risks of thromboembolic complications remain unclear. This study aimed to systematically compare the risk of Venous Thromboembolism (VTE), including Deep Vein Thrombosis (DVT), Pulmonary Embolism (PE), and Arterial Thromboembolic events in patients undergoing THR versus hemiarthroplasty for femoral neck fractures. Materials and Methods: A comprehensive literature search was conducted in PubMed, Web of Science, Google Scholar, and OVID-Medline for studies published between 2000 and 2024. Eligible studies included patients aged ≥50 years with femoral neck fractures treated with THR or Hemiarthroplasty and reported thromboembolic outcomes. Risk of bias was assessed using the Newcastle–Ottawa Scale and the RoB 2 tools. Meta-analysis was performed using RevMan software (Version 5.4). Results: Twelve studies were included in the systematic review, and ten in the meta-analysis, encompassing over 160,000 patients. THR was associated with a significantly increased risk of DVT (RR = 1.53; 95% CI: 1.40–1.68, p = 0.00001) and combined VTE (RR = 1.48; 95% CI: 1.36–1.61, p = 0.00001) compared to HA. No significant difference was observed in PE risk. Interestingly, THR was linked to a lower risk of Arterial Thromboembolic events, such as Ischemic Stroke. Conclusions: Compared with Hemiarthroplasty, THR increases the risk of VTE, including DVT, with no increased risk of PE. Surgical decisions should be guided by individual patients’ risk factors for thrombotic and cardiovascular events.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".