The Effect of PRP Augmentation of Arthroscopic Repairs of Shoulder Rotator Cuff Tears on Postoperative Clinical Scores and Retear Rates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The aim of this review and meta-analysis is to assess recent clinical trials concerning the combination of operative treatment of rotator cuff tears and the administration of PRP and its effect on clinical scores and postoperative retear rates. The trials were used to compare the combination of PRP treatment and arthroscopic rotator cuff repair to arthroscopy alone. Twenty-five clinical trials were reviewed. A risk-of-bias assessment was made for all randomized clinical trials included, using the Cochrane collaboration’s tool as well as a quality assessment for all non-randomized studies utilizing the Newcastle−Ottawa scale. The PRP-treated patients showed statistically significant improvement postoperatively compared to control groups concerning the Constant−Murley (mean difference 2.46, 95% CI 1.4−3.52, p < 0.00001), SST (mean difference 0.32, 95% CI 0.02−0.63, p = 0.04), and UCLA (mean difference 0.82, 95% CI 0.23−1.43, p = 0.07) scores. A statistically significant decrease of retear rates in the PRP-treated patients, with a risk ratio of 0.78 (95% CI 0.65−0.94, p = 0.01), was found. We believe that the results presented have positive aspects, especially concerning the retear risk, but are yet inconclusive concerning clinical results such as shoulder pain and function.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".