Long-Term Outcomes of Articular Surface Replacement (ASR) Implant in Hip Arthroplasty: A Single Institution Review
Bibliographic record
Abstract
Various metal-on-metal (MoM) total hip replacements (THRs) have been found to have high short-term failure rates due to adverse responses to metal debris (ARMD). As a consequence, several low-performing THRs have been removed off the market. The purpose of this research was to look at the at least five-year outcomes of patients who had MoM hip arthroplasty at our institution. In one specialised centre between 2007 and 2008, 24 Articular Surface Replacement (ASRTM, DePuy, Warsaw, IN, USA) MoM THRs (in 24 patients, mean age: 56.4 years) were implanted. DePuy ASR hip prosthesis for osteoarthritis or hip fractures were employed in the THR system. All patients were summoned back for a clinical assessment, and imaging was done as needed. The average period of follow-up was 8.0 years (6.0-10 years). In all, eight instances (33.3%) were discovered to have pseudotumors, four hips (16.7%) were revised, and one (4.1%) was operated for ARMD. The Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Oxford ratings improved statistically significantly five years after surgery in all three areas of pain, disability, and stiffness; however, there was no statistically significant change in the 36-Item Short Form Survey (SF-36) (mental) score. MoM hip arthroplasty had a greater revision incidence at five years in our group, presumably owing to the adoption of a smaller femoral head size.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".