The impact of scapular posture and sagittal spine alignment on motion and functional outcomes following reverse total shoulder arthroplasty: a scoping review
Bibliographic record
Abstract
Background: Reverse total shoulder arthroplasty (RTSA) has evolved beyond its initial indication for elderly patients with rotator cuff arthropathy and is now performed in younger patients for various shoulder pathologies. This surgical procedure has recently gained popularity and has been shown to result in similar functional improvements and complication rates compared to anatomical total shoulder arthroplasty. Scapular posture and sagittal spine alignment (SSPA) have recently emerged as factors potentially influencing RTSA outcomes. This scoping review aimed to assess the existing body of evidence on this topic. Methods: A systematic search was conducted on MEDLINE, Embase, and CENTRAL databases to evaluate the impact of scapular posture and SSPA on RTSA outcomes. Results: A total of 6 studies (616 shoulders) were included in this review. Scapular posture was found to influence RTSA outcomes, with studies reporting correlations between scapular posture with postoperative range of motion and functional scores. Suboptimal scapular posture, particularly type C (kyphotic posture with protracted scapulae), appeared to be associated with reduced external rotation. However, findings among the included studies regarding SSPA were varied. Some studies suggested that SSPA, notably thoracic kyphosis, might impact RTSA outcomes by influencing scapular posture, while others did not find a clear relationship. Conclusion: Scapular posture was implicated as a potential factor affecting RTSA outcomes; however, the role of SSPA remains inconclusive. There is currently a lack of high-quality evidence in the literature to draw definitive conclusions regarding the impact of scapular posture and SSPA on RTSA outcomes. More research is warranted to investigate these relationships more comprehensively.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".