Impact of Platelet-Rich Plasma on Arthroscopic Repair of Small- to Medium-Sized Rotator Cuff Tears: A Randomized Controlled Trial
Bibliographic record
Abstract
© 2016, © The Author(s) 2016. Background: Increased interest in using platelet-rich plasma (PRP) as an augment to rotator cuff repair warrants further investigation, particularly in smaller rotator cuff tears. Purpose: To examine the effectiveness of PRP application in improving perioperative pain and function and promoting healing at 6 months after arthroscopic repair of small- or medium-sized rotator cuff tears. Study Design: Randomized controlled trial; Level of evidence, 1. Methods: This was a double-blinded randomized controlled trial of patients undergoing arthroscopic repair of partial- or full-thickness rotator cuff tears of up to 3 cm who were observed for 6 months. Patients were randomized to either repair and PRP application (study group) or repair only (control group) groups. The patient-oriented outcome measures utilized were the visual analog scale (VAS), the Short Western Ontario Rotator Cuff Index (ShortWORC), the American Shoulder and Elbow Surgeons (ASES) form, and the Constant-Murley Score (CMS). Range of motion (ROM) and inflammatory and coagulation markers were measured before and after surgery. Magnetic resonance imaging was used at 6 months to assess retear and fatty infiltration rate. Results: Eighty-two patients (41 males) with a mean age of 59 ± 8 years were enrolled; 41 patients were included in each group. Both the PRP and control groups showed a significant improvement in their pain level based on the VAS within the first 30 days (P .05), retear (14% vs 18% full retear; P =.44), or fatty infiltration rate (P =.08). Conclusion: The PRP biological augmentation for repair of small- to medium-sized rotator cuff tears has a short-term effect on perioperative pain without any significant impact on patient-oriented outcome measures or structural integrity of the repair compared with control group.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".