Cortical Strut Allograft in Revision Total Hip Arthroplasty
Bibliographic record
Abstract
Fifty-two patients with significant uncontained but non-circumferential femoral bone loss were reconstructed using cortical strut allografts. The allografts used were deep frozen and irradiated with 2.5 Mrads. The mean age was 65. The average follow-up was 4.8 (1.6–10.0) years. The following radiographic parameters were studied: location and the dimension of the allografts, resorption and incorporation of the allografts, and union between the cortical strut allografts and host bone. Union between the allografts and host bone took an average of 10 months. There were 2 non-unions (union rate 96%) but no graft fractures. Severe graft resorption occurred in two cases. The overall radiographical failure rate was 8% (4/52). The process of incorporation could take over two years to complete. The average length of the strut allografts immediately post-operatively was 154 mm (66–280 mm). The length of the strut allografts at final follow-up was 143 mm (54–258 mm). The percentage decrease in the length of the struts was 8% (0–48%). The mean pre-operative and post-operative Harris hip score was 39.4 and 65.6 respectively. Six of the fifty-two patients had further femoral revision surgery (12%). None of these re-revisions was done for reasons directly related to the cortical struts. Cortical strut allografts are useful to augment uncontained, non-circumferential femoral defects. They can remodel with time to enhance femoral bone stock. They unite consistently to host bone without significant resorption.
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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 | 0.000 | 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".