Decreased complication profile and improved clinical outcomes of primary reverse total shoulder arthroplasty after 2010: A systematic review
Bibliographic record
Abstract
The purpose of this review was to update the complication profile of reverse total shoulder arthroplasty (rTSA) post-2010, given greater procedural familiarity, improved learning curves, enhanced implant designs, and increased attention to the nuances of patient selection. Three electronic databases were searched and screened in duplicate from 1 January 2010 to 16 December 2018 based on predetermined criteria. Twenty-two studies examining 1455 patients (26% male; mean age: 73.4 ± 3.6; mean follow-up: 23.4 ± 14.3 months) were reviewed. Post-operative motion ranged a mean 122.4° ± 11.5° flexion, 109° ± 19.4° abduction, and 33° ± 11.2°/41° ± 5° external/internal rotation. Post-operative mean Constant score was 58.9 ± 10.1, American Shoulder Elbow Surgeon score was 73.4 ± 6.1, Simple Shoulder Test score was 63.5 ± 6.5, and a Visual Analog Scale pain score was 1.6 ± 0.9. The overall complication rate was 18.2% and major complication rate was 15.4%. Compared to pre-2010, the overall complication rate of 18.2% is lower than previous rates of 19%-68%, with the rate of "major" complications dropping three-fold from 15.4% to 4.6%. The data suggest that rTSA is a safe and efficacious alternative to aTSA and HA, and the "stale" nature of previous complication profiles are points fundamental to perioperative discussions surrounding rTSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".