Reconstruction of chronic abductor deficiency after revision hip arthroplasty using an extensor mechanism allograft
Bibliographic record
Abstract
This study reports the clinical outcome of reconstruction of deficient abductor muscles following revision total hip arthroplasty (THA), using a fresh-frozen allograft of the extensor mechanism of the knee. A retrospective analysis was conducted of 11 consecutive patients with a severe limp because of abductor deficiency which was confirmed on MRI scans. The mean age of the patients (three men and eight women) was 66.7 years (52 to 84), with a mean follow-up of 33 months (24 to 41). Following surgery, two patients had no limp, seven had a mild limp, and two had a persistent severe limp (p = 0.004). The mean power of the abductors improved on the Medical Research Council scale from 2.15 to 3.8 (p < 0.001). Pre-operatively, all patients required a stick or walking frame; post-operatively, four patients were able to walk without an aid. Overall, nine patients had severe or moderate pain pre-operatively; ten patients had no or mild pain post-operatively. At final review, the Harris hip score was good in five patients, fair in two and poor in four. We conclude that using an extensor mechanism allograft is relatively effective in the treatment of chronic abductor deficiency of the hip after THA when techniques such as local tissue transfer are not possible. Longer-term follow-up is necessary before the technique can be broadly applied.
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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".