Bone graft incorporation failure with inappropriate limb load transfer can lead to aseptic acetabular loosening of metal-on-metal prosthesis: A case report
Bibliographic record
Abstract
BACKGROUND: Aseptic acetabular loosening can result from various factors that can be categorized into groups: patient-related, surgeon-related and implant-related. We present a case of a 63-year-old patient who at first underwent a total hip arthroplasty (THA) using a metal-on-metal bearing due to hip arthrosis. Follow-up visits revealed no complications after the procedure. Two years after the THA, acetabular component loosening occurred due to subsequent trauma of the opposite hip, necessitating a revision THA using a ceramic-on-ceramic bearing. CASE SUMMARY: We aim to illustrate a rare case where the primary reason for undergoing THA revision was not only incomplete bone graft incorporation but also improper limb load distribution. Following the revision arthroplasty, a 9-year follow-up visit revealed improvements in all evaluation measures on questionnaire compared to the state before surgery: Harris Hip Score (before surgery: 15; after surgery: 95), Western Ontario and McMaster Universities Arthritis Index (before surgery: 96; after surgery: 0), and Visual Analogue Scale (before surgery: 10; after surgery: 1). CONCLUSION: Opposite-hip trauma caused a weight transfer to the limb after a THA procedure. This process led to a stress shielding effect, resulting in acetabular component loosening.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".