The Addition of Platelet‐Rich Plasma to Scaffolds Used for Cartilage Repair: A Review of Human and Animal Studies
Bibliographic record
Abstract
PURPOSE: To review the available literature on studies focusing on platelet-rich plasma (PRP)-enhanced scaffolds for cartilage lesion repair in animals and to analyze the clinical outcomes of similar biologically augmented cartilage regeneration techniques in humans. METHODS: We conducted a literature search and subsequent review investigating the potential of PRP to enhance articular cartilage repair using scaffolds or bioengineered implants. RESULTS: Of the 14 animal model studies reviewed, 10 reported positive effects with PRP whereas only 2 showed negative overall effects. The remaining 2 studies reported no significant differences, or neutral results, with the use of PRP. With the addition of PRP, the gross appearance and histologic analysis of repair cartilage were improved or no difference was seen compared with control (11 of 12 studies that looked at this). Human studies of the knee or talar dome showed improvements in clinical assessment scores as soon as 6 months after surgery. There was great variability in the method of PRP preparation, choice of scaffold, and cell source between studies. CONCLUSIONS: PRP-augmented scaffolds have been shown to be beneficial in the articular cartilage repair process in animals and humans based on macroscopic, histologic, and biochemical analysis and based on clinical outcome scores, respectively. Comparison between studies is difficult because there is great variability in PRP preparation and administration. LEVEL OF EVIDENCE: Level IV, systematic review of Level III and IV studies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".