Outcomes and complications of failed anatomic shoulder arthroplasty revised with reverse arthroplasty: a systematic review
Bibliographic record
Abstract
BACKGROUND: Reverse shoulder arthroplasty (rTSA) is an increasingly utilized option to address complications following anatomic total shoulder arthroplasty (aTSA). This systematic review aims to identify the patient outcomes, complication rates, and re-revision rates following revision to a reverse shoulder replacement from a failed anatomic total shoulder arthroplasty. METHODS: MEDLINE, EMBASE, and CENTRAL were searched from inception until November 21, 2023 to identify studies reporting outcomes and/or complications for patients with a failed aTSA revised to an rTSA. Primary outcomes included the intra- and postoperative complication rate and secondary surgery rate. Secondary outcomes included the mean postrevision improvement in the American Shoulder and Elbow Surgeon score, pain, Constant score, Simple Shoulder test score, and range of motion. RESULTS: From 1347 records, 26 studies (730 patients) assessing rTSA following a failed aTSA were included. The most common indications for revision were rotator cuff tear (32%), loosening (12%), and instability (12%). Postoperatively, clinically important improvements were found for the American Shoulder and Elbow Surgeon score, visual analog scale pain score, Constant score, flexion, and abduction. Following revision to rTSA, complications occurred in 22% of patients, with implant failure being the most prevalent (25%). Secondary surgery after rTSA occurred in 12% of patients. CONCLUSION: The revision of a failed aTSA to an rTSA resulted in clinically significant benefits. The most common indication for aTSA revision was due to rotator cuff pathology, followed by instability and loosening. Despite the high potential for improved patient outcomes postrevision, the procedure is accompanied by a higher complication rate relative to primary rTSA, with implant failure and fractures being the most common.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".