Surgical Complications After Reverse Total Shoulder Arthroplasty and Total Shoulder Arthroplasty in the United States
Bibliographic record
Abstract
INTRODUCTION: Shoulder arthroplasty has become popular in the treatment of degenerative shoulder conditions in the United States. Shoulder arthroplasty usage has expanded to younger patients with increased surgical indications. METHODS: Reverse total shoulder arthroplasty (RTSA) and TSA patient records with the 1-year follow-up between 2015 and 2018 were queried from the nationwide PearlDiver Mariner Shoulder Database using International Classification of Disease-10 codes. Chi-square analysis was done to compare the demographics, surgical complications, and revision procedures between RTSA and TSA. RESULTS: From 2010 to 2018, there was an increase in shoulder arthroplasty cases because of RTSA. The overall surgical complication and revision procedure rates were 2.26% and 3.56% for RTSA, and 6.36% and 2.42% for TSA. Patients older than 50 years had statistically lower surgical complications after RTSA than TSA (2.25% versus 3.94%, P < 0.05), whereas no statistical difference between RTSA and TSA for patients younger than 50 years (10.06% versus 7.45%, P = 0.19). Male patients had higher RTSA complication rates (3.12% versus 2.28%, P < 0.05), whereas female patients had higher TSA (4.86% versus 5.92%, P < 0.05). History of tobacco, depression, and obesity were risk factors for higher complications. CONCLUSION: RTSA has become more commonly done than TSA in the United States. Older patients who underwent shoulder arthroplasty had lower surgical complication. TSA had a higher surgical complication rate than RTSA for patients older than 50 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".