Management of irreparable subscapularis tears: Current concepts
Bibliographic record
Abstract
Primary repair of acute subscapularis (SSC) tears provides excellent results, but tendon retraction, muscle atrophy, fatty infiltration, and humeral head migration may render a more chronic tear irreparable. These irreparable SSC tears present a diagnostic and treatment challenge for orthopaedic surgeons. Careful physical examination and imaging evaluation can help to distinguish those with reparable versus irreparable tears, but they are still not very reliable due to the methodological limitations of current evidence. Therefore, future research using 3D and quantitative measurement techniques is necessary to better predict the irreparability of the SSC. When conservative treatment of an irreparable SSC tear fails, reversed shoulder arthroplasty has been established as the preferred treatment option for older, low-demand patients with arthropathy, providing reliable improvements in pain and function. In younger patients without significant arthropathy, musculotendinous transfers are the treatment of choice. The pectoralis major transfer is historically the most frequently performed procedure and provides improved range of motion and pain relief, but fails to adequately restore strength and shoulder function. The latissimus dorsi transfer has gained increased interest over the last few years due to its biomechanical superiority, and early clinical studies suggest improved outcomes as well. More recently, anterior capsular reconstruction has been proposed as an alternative to musculotendinous transfers, but clinical data are completely lacking. Future high-quality randomised controlled trials are necessary to reliably compare the different musculotendinous transfers and anterior capsular reconstruction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".