Low risk of periprosthetic fracture and subsidence with automated impaction device in arthroplasty following femoral neck fracture: A retrospective study
Bibliographic record
Abstract
Background: Manual broaching in hip arthroplasty for femoral neck fractures (FNFx) may introduce variability in force application, increasing the risk of periprosthetic fractures. Automated impaction devices (AID) deliver consistent, uniform force vectors during femoral preparation, potentially reducing implant-bone mismatch, stem subsidence, and fracture risk. This study evaluates early complication rates following AID use in hip arthroplasty for FNFx. Methods: A retrospective cohort study was conducted on consecutive patients undergoing total hip arthroplasty (THA) or hemiarthroplasty (HA) for FNFx by two surgeons between January 1, 2019, and June 1, 2023. All patients received a cementless femoral stem implanted with an AID and had a minimum 30-day follow-up. Outcomes assessed included 30-day revision-free stem survivorship, intraoperative and postoperative periprosthetic fractures, femoral component subsidence, and 30-day reoperation and readmission rates. Results: The cohort included 118 patients [72.0 % women; mean age 77.4 (range, 52-95) years], with 82 (69.5 %) undergoing THA and 36 (30.5 %) HA. One intraoperative fracture (0.8 %, Vancouver AG) and one postoperative fracture (0.8 %, Vancouver B1) occurred. The 30-day reoperation rate was 3.4 % (n = 4), and the readmission rate was 16.3 % (n = 15). Mild femoral component subsidence occurred in 1.7 % (n = 2) without evidence of loosening. No femoral stem revisions were reported within 30 days, yielding a 100 % short-term survivorship rate. Conclusion: Early outcomes suggest that AID use in hip arthroplasty for FNFx is associated with low rates of periprosthetic fracture, subsidence, and early complications. Further prospective studies are needed to assess long-term outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".