A novel approach to medial acetabular wall reconstruction in revision total hip arthroplasty using screwless porous metal augments: A report of two cases
Bibliographic record
Abstract
Background Medial acetabular wall defects present unique reconstruction challenges in revision total hip arthroplasty (rTHA). Traditional approaches using bone grafts risk failure due to reabsorption due to poor vascular supply or minimal loading, while metal augments typically require screw fixation that poses neurovascular risks in the medial wall. We present a novel technique using screwless porous metal augments in medial acetabular reconstruction. Case report Two patients underwent rTHA for medial acetabular defects. Case 1 involved a 63-year-old woman with acetabular fracture and progressive protrusion after initial revision. Case 2 was an 85-year-old man with component loosening and medial migration. Both were reconstructed using free-floating trabecular metal augments placed within medial defects, supported by cancellous allograft and overlying porous metal cups. Short-term follow-up (3–6 months) demonstrated stable component positioning without migration in both cases. Conclusion Screwless porous metal augments represent a promising alternative for medial acetabular reconstruction, avoiding neurovascular risks while potentially providing an osteoconductive scaffold for bone restoration. Longer-term studies are needed to confirm biological integration and clinical durability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".