A review of the New Zealand National Joint Registry to evaluate the survivorship and revision rates of Nexel and Coonrad-Morrey total elbow arthroplasty
Bibliographic record
Abstract
BACKGROUND: Total elbow arthroplasty (TEA) is an appropriate surgical treatment option for a variety of conditions ranging from inflammatory arthritis to trauma. Because of a high complication profile, implant companies have attempted to improve patient outcomes with evolving design mechanics and philosophy. However, the Nexel TEA prosthesis has been criticized for its unacceptably high revision rate by other research groups in the literature. The purpose of this study was to evaluate the survivorship and revision rates of the Nexel and Coonrad-Morrey TEA implant systems in New Zealand. METHODS: Prospectively collected National Joint Registry data were used to compare the survival rates of these prostheses. Underlying diagnoses, reasons for revision, and patient demographics were all recorded. Statistical analysis included survival analysis using Kaplan-Meier curves and comparison between groups using independent t tests. RESULTS: Over the 23-year study interval, the Nexel and Coonrad-Morrey prostheses showed similar survivorship and revision rates. The revision rates at 5 years were 7.3% for Nexel and 4.5% for the Coonrad-Morrey cohorts. The average time to revision for those who are revised was 3.13 ± 1.74 years in the Nexel group and 4.93 ± 4.13 years in the Coonrad-Morrey population. CONCLUSION: Our study confirms a lower revision rate of the Nexel TEA compared to other studies in the literature. Additionally, the Nexel TEA implant performs comparably to its predecessor, the Coonrad-Morrey prosthesis in New Zealand. Although it is difficult to explain the discrepancy in results with the study by Morrey et al, future studies should focus on investigating postoperative radiographs and a deep analysis of the specific surgical technique used for this implant.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".