Cartilage Regeneration Potential of Platelet-Rich Plasma versus Plasma Rich in Growth Factors in Knee Osteoarthritis Based on Serum Biomarker of Cartilage Degradation, Coll2-1: A Triple-Blind Randomized Study
Bibliographic record
Abstract
Background: Through the course of the reported triple-blind randomized single-center study, we compared the clinical effectiveness of platelet-rich plasma (PRP) and plasma rich in growth factors (PRGF) and evaluated their cartilage regeneration potential by studying the change in blood biomarkers related to cartilage breakdown, Coll2-1 before and after the intervention. Materials and Methods: During our study, the candidates with symptomatic KL grade 2 and 3 knee osteoarthritis were recruited and randomized into two groups. Each group received three intra-articular injections of PRP or PRGF, each one 4 weeks apart. Baseline visual analog scale (VAS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were compared with scores reported before 2nd and 3rd injection and at the 6-month and 12-month follow-ups since the first intervention. Furthermore, baseline serum Coll2-1 levels were compared with those obtained at the 12-month follow-up since the first intervention. Results: The difference between the baseline means serum Coll2-1 values and the mean value obtained at the 12-month follow-up was found to be significant in both the PRP (P = 0.005) and PRGF (P = 0.009) groups showing significant reductions in the Coll2-1 levels at the 12-month follow-up of the patient. Conclusion: Both PRGF and PRP showed statistically significant reductions in serum Coll2-1 levels at 12 months. PRGF showed better VAS and WOMAC outcomes at the 12-month follow-up as compared to PRP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".