Management of massive acetabular bone defects in revision arthroplasty of the hip using a reconstruction cage and porous metal augment
Bibliographic record
Abstract
AIMS: It may not be possible to undertake revision total hip arthroplasty (THA) in the presence of massive loss of acetabular bone stock using standard cementless hemispherical acetabular components and metal augments, as satisfactory stability cannot always be achieved. We aimed to study the outcome using a reconstruction cage and a porous metal augment in these patients. PATIENTS AND METHODS: A total of 22 acetabular revisions in 19 patients were performed using a combination of a reconstruction cage and porous metal augments. The augments were used in place of structural allografts. The mean age of the patients at the time of surgery was 70 years (27 to 85) and the mean follow-up was 39 months (27 to 58). The mean number of previous THAs was 1.9 (1 to 3). All patients had segmental defects involving more than 50% of the acetabulum and seven hips had an associated pelvic discontinuity. RESULTS: Three failures were observed in two hips, both of which had undergone a previous resection of a tumour affecting the acetabulum. Other complications included a late arterial injury, a sciatic nerve palsy, a dislocation treated with a femoral revision, a deep infection treated with irrigation and debridement and a fracture of the greater trochanter treated conservatively. The mean Oxford Hip Score significantly increased from 13.9 (2 to 23) to 28.7 (13 to 38) (p < 0.00001). The mean vertical distance between the centre of rotation of the hip and its normal location decreased from 30 mm to 10 mm. CONCLUSIONS: 2017;99-B:607-13.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".