Revision of the Deficient Proximal Femur With a Proximal Femoral Allograft
Bibliographic record
Abstract
UNLABELLED: Substantial bone loss is frequently encountered with revision hip arthroplasty. A proximal femoral allograft may be used to reconstitute bone stock in the multiply revised femur with segmental bone loss of greater than 5 cm. We retrospectively reviewed 92 patients (93 hips) who underwent such proximal femoral allografts. The average age at the surgery was 61 years. The average number of previous revision procedures was 2.5. Six patients were lost to followup. Thirty-four of 36 deceased patients had the original proximal femoral allograft at the time of death. The minimum followup for the 50 remaining patients was 15 years (average, 16.2 years; range, 15-22 years). Analysis included survivorship and radiographic assessment. Of the 50 patients reviewed, two had a failed reconstruction due to infection, six for aseptic loosening, three for nonunion, and four for dislocation. Revision of the proximal femoral allograft for all reasons excluding the acetabulum was performed in seven patients. At last followup, 42 patients (84%) had a well-functioning construct. Proximal femoral allograft for revision hip arthroplasty in femoral segmental bone loss is a durable alternative in most patients for a complex problem. LEVEL OF EVIDENCE: Level IV, therapeutic study. See the 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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".