Is revision to anatomic shoulder arthroplasty still an option? A systematic review
Bibliographic record
Abstract
Background: With the historical complications when using total shoulder arthroplasty (TSA) to revise failed arthroplasties, and the success of the reverse prosthesis in the revision setting, the question arises whether revision to TSA is still a reasonable option? This systematic review examines revision to TSA and the factors associated with outcomes. Methods: A systematic review was performed for studies of TSA used to revise a failed hemiarthroplasty or TSA. The primary outcome was implant failure leading to a repeat revision arthroplasty. Secondary outcomes included visual analog scale (VAS) pain scores, shoulder motion and other clinical outcomes of shoulder function. Data were pooled to generate representative frequency-weighted means. Results: Thirteen studies were included, totaling 312 shoulders. Etiologies for revision included glenoid arthrosis (62%), glenoid component failure (36%), and other (2%). Of which, 39% of cases experienced complications and 12% required another arthroplasty revision. Secondary outcomes such as VAS pain, Constant, ASES and UCLA score improved, but none were statistically significant. Unsatisfactory outcomes were higher among patients with glenoid bone loss, instability, and soft tissue deficiencies. Discussion: Revision to anatomic TSA can be an acceptable option in certain patients. However, the high rate of complications and glenoid loosening, makes this a limited approach for a revision to anatomic TSA procedure.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".