Birmingham Hip Resurfacing Using a Novel Mini-navigation System: A Case Report.
Bibliographic record
Abstract
INTRODUCTION: Inaccurate positioning of acetabular and femoral components during Birmingham Hip Resurfacing (BHR) can lead to increased wear, edge-loading, and failure of the prosthesis, a consequence of substantial concern for young and active patients seeking long- term, post-operative survival of the joint. In turn, sizing of the acetabular component during BHR is limited by the size of the native femoral neck, and reaming of the acetabulum should be minimized to optimize the bony architecture for potential subsequent arthroplasties. Computer-assisted navigation systems (CAS) can improve the accuracy of component selection and positioning during total hip arthroplasty (THA); however, evidence for the usefulness of CAS in BHR is lacking. The present report summarizes a case of BHR performed with navigation to assist with component positioning. CASE REPORT: A 34-year-old male martial arts instructor presented with a constant and localized pain in the left hip and groin. Following the examination, the patient was diagnosed with left hip impingement and osteoarthritis. Due to his age and active lifestyle, the patient elected to undergo BHR rather than THA. The navigation tool was used to assist with acetabular reaming and to confirm final cup placement. Post- operatively, standard, anteroposterior pelvic radiographs showed a final cup position of 39.0° inclination and 24.7° anteversion, which was confirmed by the navigation tool. A pre-operative leg length differential of 3mm was measured from pre-operative radiographs; however, leg lengths were equalized following BHR. CONCLUSION: This report summarizes a case of BHR performed in a young, active patient with the assistance of a novel surgical navigation tool. The use of the navigation device allowed for more accurate acetabular preparation and component positioning, maximizing the bone-sparing characteristics of BHR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".