The role of bone marrow stimulation in rotator cuff repair: a systematic review and meta‐analysis
Bibliographic record
Abstract
PURPOSE: The objective of this study was to investigate whether RCR (rotator cuff repair) with BMS (bone marrow stimulation) can provide a lower retear rate and better shoulder function than arthroscopic RCR alone in rotator cuff tear (RCT) patients. METHOD: The PubMed, Cochrane Library, EMBASE and Web of Science databases were searched until Feb 2022. Risk of bias for randomized controlled trials was evaluated by two independent reviewers with Cochrane collaboration risk bias of tool, and that for cohort studies was evaluated with the Newcastle-Ottawa Scale (NOS). The primary outcome was the retear rate. Secondary outcomes included the American Shoulder and Elbow Surgeons (ASES) score, University of California, Los Angeles Shoulder Scale (UCLA) score, Constant-Murley score (CMS) and visual analogue scale (VAS) score. Subgroup analysis was performed to explore the effect of suture method and tear size on BMS procedure. RESULT: Five randomized controlled trials and four cohort studies with a total of 827 patients were included. The pooled retear rate between the RCR with BMS group and the RCR alone group was significantly different (17.5% vs. 28.9%; P < 0.0001). There were no differences in the ASES score, UCLA score and VAS score. The CMS was significantly higher in RCR with BMS group than the RCR alone groups (P = 0.02), while the difference was well below the MCID. RCR with BMS resulted in a significantly lower retear rate than RCR alone for large and massive RCTs (19.7% vs. 32.5%; P = 0.01). CONCLUSION: Compared with RCR alone, RCR with BMS can significantly reduce the retear rate in arthroscopic RCT patients while not clinically relevant differences were found. BMS may further reduce the retear rate of large and massive RCTs. LEVEL OF EVIDENCE: Level III; Systematic Review and Meta-analysis.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".