Structural Allograft as an Option for Treating Infected Hip Arthroplasty with Massive Bone Loss
Bibliographic record
Abstract
BACKGROUND: Revision of the infected hip arthroplasty with major bone loss is difficult. Attempts to restore bone stock with structural allograft are controversial. QUESTIONS/PURPOSES: We assessed the (1) reinfection rate; (2) rerevision rate; (3) radiographic graft union, resorption, and implant migration; (4) Harris hip scores at 1 year and at last followup compared with before surgery; and (5) other major complications associated with the use of bulk structural allograft to treat massive bone loss in infected hip arthroplasty. METHODS: We retrospectively reviewed 27 patients who underwent two-stage revision arthroplasty using structural allograft to treat massive bone defects in infected hip arthroplasty. There were 17 proximal femoral grafts, three acetabular major column grafts, two acetabular minor column grafts, and 10 cortical strut grafts used. Five patients had combinations of two allografts. The minimum followup was 1.1 years (mean, 8.2 years; range, 1.1-16.8 years). RESULTS: One of 27 patients had reinfection. The Kaplan-Meier survivorship was 93% at 10 years with rerevision for aseptic loosening as the end point. Radiographically, three patients had nonunion at the graft-host junction. All patients except two had graft resorption, of which all were mild except two, which were severe. Three patients had implant migration. The mean modified Harris hip scores were 39.2 points (range, 25-60) preoperatively, 67.3 points (range, 40-91) at 1-year followup, and 70.3 points (range, 46-81) at last followup. Other major complications included one patient with dislocation and one patient with transient sciatic nerve injury. CONCLUSIONS: Based on our data, we believe the use of structural allografts is a reasonable option for treating massive bone loss in infected hip arthroplasties. LEVEL OF EVIDENCE: Level IV, therapeutic study. See Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".