Anatomic total shoulder arthroplasty in rheumatoid arthritis: A systematic review
Bibliographic record
Abstract
Purpose: Given the poor soft-tissue quality in rheumatoid arthritis patients, many believe that rheumatoid arthritis should be treated with reverse total shoulder arthroplasty (rTSA). The purpose of this paper is to systematically assess outcomes of anatomic total shoulder arthroplasty (aTSA) in rheumatoid arthritis to determine if aTSA remains a viable option. Methods: A comprehensive literature search was conducted identifying articles relevant to aTSA in the setting of rheumatoid arthritis with intact rotator cuff. Outcomes include clinical outcomes and rates of complication and revision. Results: Ten studies were included with a total of 279 shoulders with mean follow-up of 116 ± 69 months. The mean age was 68 ± 10 years. Survivorship was 97%, 97% and 89% at 5, 10 and 20 years, respectively. The overall complication rate was 9%. Radiolucency was present in 69% of patients, of which 34% were at risk of loosening at 79 months. The overall rate of revision was 8.4%. Studies generally reported clinically significant improvements in range of motion, Constant score and ASES score. Conclusion: aTSA in the rheumatoid patient results in improvements in range of motion and patient-reported outcomes. Rates of complications and survivorship are generally good in this population. However, it should be noted that there is significant heterogeneity in outcome reporting amongst the literature on this topic and that many studies fail to adequately report complication and revision rates. When compared to rTSA in patients with rheumatoid arthritis, evidence suggests that aTSA is still a viable treatment option despite the shift in utilization to rTSA.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".