Segmental proximal femoral bone loss and revision total hip replacement in patients with developmental dysplasia of the hip
Bibliographic record
Abstract
The treatment of substantial proximal femoral bone loss in young patients with developmental dysplasia of the hip (DDH) is challenging. We retrospectively analysed the outcome of 28 patients (30 hips) with DDH who underwent revision total hip replacement (THR) in the presence of a deficient proximal femur, which was reconstructed with an allograft prosthetic composite. The mean follow-up was 15 years (8.5 to 25.5). The mean number of previous THRs was three (1 to 8). The mean age at primary THR and at the index reconstruction was 41 years (18 to 61) and 58.1 years (32 to 72), respectively. The indication for revision included mechanical loosening in 24 hips, infection in three and peri-prosthetic fracture in three. Six patients required removal and replacement of the allograft prosthetic composite, five for mechanical loosening and one for infection. The survivorship at ten, 15 and 20 years was 93% (95% confidence interval (CI) 91 to 100), 75.5% (95% CI 60 to 95) and 75.5% (95% CI 60 to 95), respectively, with 25, eight, and four patients at risk, respectively. Additionally, two junctional nonunions between the allograft and host femur required bone grafting and plating. An allograft prosthetic composite affords a good long-term outcome in the management of proximal femoral bone loss in revision THR in patients with DDH, while preserving distal host bone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".