Acetabular Augments Used in Revision Hip Arthroplasty: Minimum 10-year Follow-Up of Implant Survivorships, Functional Scores, and Radiographic Outcomes
Bibliographic record
Abstract
BACKGROUND: Acetabular bone loss is a major challenge in the setting of revision total hip arthroplasty (THA). Porous tantalum augments have emerged as a viable solution to acetabular bone loss in revision THA. The purpose of this study was to evaluate the survivorship, clinical, and radiological outcomes of these implants. METHODS: We identified 104 augment implants from our retrospective chart review of revision THA from June 2003 to July 2013. Of these patients, 75 (72.1%) were women, the mean age at surgery was 66 years (range, 27 to 87), and the mean follow-up was 13.2 years (range, 0.25 to 18.2). Kaplan-Meier survival analysis was performed, with failure defined as revision for aseptic loosening of the acetabular reconstruction. RESULTS: There was significant improvement in the Harris Hip Score from 40.0 to 77.3 (P < 0.001) and the Oxford Hip Score from 14.9 to 36.3 (P < 0.001). Survivorship for failure due to aseptic loosening was 98.8% (95% CI [confidence interval] 96.4 to 100) at 24 months with 60 hips at risk, and 90.4% (95% CI 83.0 to 97.8) at 60 and 120 months with 38 and 18 hips at risk, respectively. The overall number of complications was 34 (32.7%). Of these complications, 21 (20.2%) required repeat revision surgery. The revision rate due to aseptic loosening of the augment, infection, dislocation, aseptic loosening of the femoral component, reconstruction failure, and heterotopic ossification was seven (6.7%), five (4.8%), four (3.8%), two (1.9%), two (1.9%), and one (0.96%), respectively. CONCLUSIONS: Treatment of acetabular defects during revision THA using porous tantalum augments provides acceptable implant survivorship and favorable clinical outcomes at mid-term (5 to 10 years) and long-term (> 10 years) follow-up.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".