Outcome of hip resurfacing revision through the Hueter-anterior approach
Bibliographic record
Abstract
BACKGROUND: The Hueter-Anterior Approach (HAA) with its limited soft tissue and internervous dissection has been shown to be an effective approach for primary total hip and hip resurfacing arthroplasty (HRA). The purpose of this study is to evaluate the clinical outcome of patients requiring revision of HRA to total hip replacement using the HAA, assessing function and complications. METHODS: We performed a retrospective review of a prospectively maintained research database. Between 2006 and 2015, 555 primary metal-on-metal (MoM) HRAs were performed via the HAA; we identified 33 hips in 30 patients that required revisions for aseptic causes to THA: aseptic loosening of acetabulum in 12 and femoral in 7, 10 for pseudotumour/ALTR, 4 for femoral neck fracture. All revision surgeries were performed through a HAA by a single surgeon who had also performed the index operation. PROMs were collected preoperatively and yearly at various timepoints postoperatively. RESULTS: The mean age at time of revision was 48.9 years (±5.3 SD) for 22 males (67%) and 11 females (33%). The mean time to revision surgery/failure of hip resurfacing was 3.3 years (±2.4 SD). There were 5 major reoperations with 3 infections, 1 acetabular loosening and 1 trunnionosis. There were significant improvements in multiple PROMs. CONCLUSIONS: The HAA is a viable surgical approach for revision of HRA with smaller initial HRA acetabular components generally requiring a relatively larger acetabular compoent at time of revision. Patients reported improvement in symptoms and function and a lower risk of subsequent reoperation than what has previously been reported for failed MoM bearings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".