Reverse shoulder arthroplasty: State-of-the-art
Bibliographic record
Abstract
The reverse shoulder arthroplasty conceived by Paul Grammont in 1985 has gradually gained popularity as a treatment for multiple shoulder diseases. Unlike previous reverse shoulder prosthesis characterized by unsatisfactory results and a high glenoid implant failure rate, the Grammont design has immediately shown good clinical outcomes. This semi constrained prosthesis solved the issues of the very first designs by medializing and distalizing the center of rotation with an increased stability of the replacement of the component. The indication was initially limited to cuff tear arthropathy (CTA). It has then been expanded to irreparable massive cuff tears and displaced humeral head fractures. The most frequent problems of this design are a limited postoperative external rotation and scapular notching. Different modifications to the original Grammont design have been proposed with the aim of decreasing the risk of failure and complications and improving the clinical outcomes. Both the position and version/inclination of the glenosphere and the humeral configuration (e.g. neck shaft angle) influence the RSA outcomes. A lateralized glenoid (whether with bone or metal) and a 135° Inlay system configuration leads to a moment arm which is the closest to the native shoulder. Clinical research will focus on implant designs reducing bone adaptations and revision rate, strategies to prevent more effectively infections. Furthermore, there is still room for improvement in terms of better postoperative internal and external rotations and clinical outcomes after RSA implanted for humeral fracture and revision shoulder arthroplasty.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".