Radial head arthroplasty: fixed-stem implants are not all equal—a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Numerous fixed-stem implants exist for radial head arthroplasty; therefore, we conducted a systematic review to compare the safety and efficacy of different types of fixed-stem implants. METHODS: We conducted a literature search, updated from a previous systematic review, to identify studies evaluating a fixed-stem radial head arthroplasty implant for any indication. We extracted data on revision rates, specific complications, and functional scores. We pooled results across studies using a random-effects method, using proportions for dichotomous data and mean values for functional scores. We analyzed outcomes by indication and specific implant. RESULTS: We included 31 studies. Studies included patients with radial head fractures only, terrible-triad injuries, or Essex-Lopresti injuries or included a heterogeneous population. We identified 15 different fixed-stem implants. The results of our analysis revealed that patients with terrible-triad injuries may be at an increased risk of revision and instability and patients with Essex-Lopresti injuries may be at an increased risk of arthritis, capitellar erosion, and osteolysis. After removing these outliers and pooling the results by specific device, we observed variability across devices in the rates of revision, arthritis, capitellar erosion, instability, and osteolysis, as well as in functional scores. CONCLUSION: Differences were seen across different implants in revision rates, certain complications, and functional scores. This study highlighted that these devices should be evaluated within the context of the patient population under examination, as patients with Essex-Lopresti or terrible-triad injuries may demonstrate worse outcomes relative to those with a fracture only.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".