A Systematic Review of the Use of Platelet-Rich Plasma in Sports Medicine as a New Treatment for Tendon and Ligament Injuries
Bibliographic record
Abstract
OBJECTIVE: To evaluate, through a systematic review of the current literature, the evidence-based outcomes of the use of platelet-rich plasma (PRP) for the treatment of tendon and ligament injuries. DATA SOURCES: A search of English-language articles was performed in PubMed and EMBASE using keywords "PRP," "platelet plasma," and "platelet concentrate" combined with "tendon" and then "ligament" independently. The search was conducted through September 2010. STUDY SELECTION: Search was limited to in vivo studies. Nonhuman studies were excluded. Tissue engineering strategies, which included a combination of PRP with additional cell types (bone marrow), were also excluded. Articles with all levels of evidence were included. Thirteen of 32 retrieved articles respected the inclusion criteria. DATA EXTRACTION: The authors reviewed and tabulated data according to the year of study and journal, study type and level of evidence, patient demographics, method of PRP preparation, site of application, and outcomes. DATA SYNTHESIS: The selected studies focused on the application of PRP in the treatment of patellar and elbow tendinosis, Achilles tendon injuries, rotator cuff repair, and anterior cruciate ligament (ACL) reconstruction. Seven studies demonstrated favorable outcomes in tendinopathies in terms of improved pain and functional scores. In 3 studies on the use of PRP in ACL reconstruction, no statistically significant differences were seen with regard to clinical outcomes, tunnel widening, and graft integration. One study examined the systemic effects after the local PRP application for patellar and elbow tendinosis. CONCLUSIONS: Presently, PRP use in tendon and ligament injuries has several potential advantages, including faster recovery and, possibly, a reduction in recurrence, with no adverse reactions described. However, only 3 randomized clinical trials have been conducted.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".