Complications of Superior Capsule Reconstruction for the Treatment of Functionally Irreparable Rotator Cuff Tears: A Systematic Review
Bibliographic record
Abstract
Abstract Purpose The purpose of this systematic review is to characterize the complications associated with superior capsule reconstruction (SCR) for the treatment of functionally irreparable rotator cuff tears (FIRCTs). Methods This systematic review was completed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Two independent reviewers completed a search of PubMed, Embase, and Medline databases. Studies were deemed eligible for inclusion if they reported postoperative outcomes of arthroscopic SCR for FIRCTs and considered at least 1 postoperative complication. Statistical heterogeneity was quantified via the I 2 statistic. Due to marked heterogeneity, pooled proportions were not reported. All complications and patient‐reported outcomes were described qualitatively. Results Fourteen studies met the inclusion/exclusion criteria. The overall complication rate post‐SCR ranged from 5.0% to 70.0% ( I 2 = 84.9%). Image‐verified graft retear ranged from 8% to 70%, I 2 = 79.4%), with higher rates reported when SCR was performed using allograft (19%‐70%, I 2 76.6%) compared to autograft (8%‐29%, I 2 = 66.1%). Reoperation (0%‐36%, I 2 = 73.4%), revision surgeries (0%‐21%, I 2 = 81.2%), medical complications (0%‐5%, I 2 = 0.0%), and infections (0%‐5%, I 2 = 0.0%) were also calculated. Conclusions SCR carries a distinct complication profile when used for the treatment of FIRCTs. The overall rate of complications ranged from 5.0% to 70.0%. The most common complication is graft retear with higher ranges in allografts (19%‐70%) compared to autografts (8%‐29%). The majority of studies reported at least 1 reoperation (range, 0%‐36%), most commonly for revision to reverse shoulder arthroplasty. Level of Evidence Level IV, systematic review of Level IV or better investigations.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".