Effectiveness of Hydrolyzed Collagen Peptide Injection for the Treatment of Collateral Ligament Pain: A Randomized Controlled Trial
Bibliographic record
Abstract
Purpose: Collateral ligament pain is a musculoskeletal condition causing knee inflammation, often affecting athletes. Despite various treatments being available, there is failure in addressing the pathophysiologic collagen degradation. Hydrolyzed collagen peptides may help damage tissues in the repairment process, but until now no data is available on local injection of collagen peptides, with data available only on oral supplementation. Methods: A randomized controlled trial assessed the efficacy of hydrolyzed collagen peptide injections (Tiss’You, Republic of San Marino) for persistent collateral ligament pain. Sixty-two patients with ultrasound-confirmed inflammation were divided into two groups. The study group (31 patients) received oral painkillers and a collagen peptide injection, while the control group (31 patients) received oral painkillers and a depo-medrol injection. Results: The study group showed significant improvement in pain relief, functional status, and quality of life, measured by the Visual Analogue Scale and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) after six months. Ultrasound confirmed ligament healing in both groups with no observed differences. The study group reported higher satisfaction and no adverse effects. Conclusion: Hydrolyzed collagen peptide injections may effectively and safely treat collateral ligament pain. This study offers insights into this novel emerging treatment option, exploiting the ability of collagen peptides in helping tissue repair. Further research with larger samples and longer follow-up is necessary to validate these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".