Arthroscopic Superior Capsular Reconstruction for Massive, Irreparable Rotator Cuff Tears: A Systematic Review of Modern Literature
Bibliographic record
Abstract
PURPOSE: To systematically review and evaluate the efficacy and complication profile of superior capsular reconstruction (SCR) as a technique to address massive, irreparable rotator cuff tears (MIRCTs). METHODS: Searches of the Cochrane Database of Systematic Reviews, Embase, MEDLINE, PubMed, and conference abstracts of 4 major conferences identified clinical studies addressing SCR for MIRCTs. Two reviewers independently screened the titles, abstracts, and full texts, extracting data from eligible studies. Reported outcome measures and complications were descriptively analyzed. RESULTS: A total of 10 studies, 7 full texts and 3 conference abstracts, satisfied the inclusion criteria. The included studies examined a total of 350 shoulders with a mean patient age of 60.6 years and mean follow-up period of 20.6 months postoperatively. Only 4 studies had a minimum of 24-month follow-up data. Statistically significant improvements in pain and function were noted in all studies reporting results, with mean improvement ranging from 29.4 to 68.5 and from 2.5 to 5.9 points across the American Shoulder and Elbow Surgeons score and visual analog scale score, respectively. Mean improvement in range of motion ranged from 21.7° to 64.0° in elevation and from 9.0° to 15.0° in external rotation. Statistically significant improvements in the postoperative acromiohumeral distance were noted in 4 of 5 reporting studies, with a mean increase ranging from 2.2 to 5.0 mm. The combined clinical and radiographic failure and/or retear rate ranged from 3.4% to 36.1%. Complications for all studies included deep infection (0%-2%), symptomatic suture anchor loosening (0%-4%), and severe shoulder contracture (0%-2%). CONCLUSIONS: Arthroscopic SCR represents an accepted surgical option for patients with MIRCTs, with short-term improvements shown in pain, range of motion, and function. Although early results are promising, further studies are necessary to determine the long-term success of this technique and to better delineate the clinical indications, survivorship, and risk factors for failure in this population. LEVEL OF EVIDENCE: Level IV, systematic review of Level III and IV studies.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".