Return to Sport After Reverse Shoulder Arthroplasty: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Reverse shoulder arthroplasties (RSAs) have become significantly more common in recent decades, and shoulder arthroplasties are increasingly performed on younger, active patient populations. However, the body of evidence evaluating return to sport after RSA is limited. OBJECTIVE: To evaluate the rates of return to sport after RSA as well as patient-reported outcomes for pain and function. DATA SOURCES: A comprehensive search of MEDLINE, EMBASE, and CENTRAL identified studies from inception through October 10, 2023. STUDY SELECTION: Studies evaluating return to sport after RSA in adult patients (>18 years) were included. Reviews, meta-analyses, non-English language, and studies that did not report return to sport data were excluded. Outcomes included rates of return to sport at any level, and at the same level or higher, as well as patient-reported outcomes. STUDY DESIGN: Systematic review. LEVEL OF EVIDENCE: Level 4. DATA EXTRACTION: A descriptive analysis of the included studies was performed. RESULTS: A total of 19 studies (3092 patients) were included. Mean patient age was 72.5 years, and most (58.8%) were female. The mean rate of return to sport at any level was 85.1% (95% CI, 84.6-85.6). The mean rate of return to the same level or higher was 69.5% (95% CI, 67.6-71.4). The mean decrease in visual analog scale pain score was 1.97, whereas ASES and CMS scores increased 64.0 and 35.5 points, respectively. All changes in patient-reported outcomes exceeded the minimal clinically important difference. CONCLUSION: This review demonstrates a high rate of return to sport after RSA, with a substantial portion of patients maintaining or exceeding their preoperative activity level. Clinically meaningful improvements in pain and function were also observed. Limitations include the predominantly retrospective study design and the high mean patient age, necessitating further research on generalizability and long-term outcomes, particularly in younger populations.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".