Acromioplasty combined with arthroscopic rotator cuff repair can reduce the risk of reoperation: a systematic review and meta-analysis
Bibliographic record
Abstract
Whether to perform acromioplasty in arthroscopic rotator cuff repair (ARCR) is controversial, and the optimal surgical approach for rotator cuff tear repair is unknown. The purpose of this study was to compare the reoperation rate, retear rate and patient-reported outcomes (PROs) of ARCR with those of ARCR combined with acromioplasty (ARCR-A). PubMed, Embase and Cochrane Library were searched for relevant literature dated between database inception and 4 December 2023. The primary outcomes of this study were the reoperation rate and the retear rate. The secondary outcomes were PROs, including the visual analogue scale (VAS) pain score, the American Shoulder and Elbow Surgeons (ASES) score, the University of California-Los Angeles (UCLA) score, the Constant score and the Western Ontario Rotator Cuff (WORC) score. The quality of the included studies was evaluated by using the risk of bias assessment tool. RevMan 5.3 software was used for meta-analysis. Fixed (I2 <50%) or random (I2 ≥50%) effects models were applied to calculate the effect size. Meta-analysis revealed that ARCR-A had a lower reoperation rate (OR = 0.35, 95%CI: 0.15–0.85, p = 0.02), but the difference in the retear rate between ARCR-A and ARCR was not significant (p = 0.25). In type 2 acromion patients, the reoperation rate was not significantly different between ARCR and ARCR-A (p = 0.12), but, for type 3 acromion patients, the retear rate was lower for ARCR-A than for ARCR (OR = 0.12, 95%CI: 0.01–0.94, p = 0.04). There were statistically significant differences in the 6-month postoperative Constant scores (p < 0.001), VAS pain scores (p = 0.003) 12-month postoperative ASES scores (p = 0.02) and 24-month postoperative WORC scores (p = 0.04), but these differences were not clinically significant. Combining ARCR with acromioplasty can reduce the rate of reoperation, especially in patients with type 3 acromion, but it provides no clinically important change in the retear rate and postoperative PRO compared with ARCR.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 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 | 0.002 |
| 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".