Intra-articular Injection of Type I Atelocollagen to Alleviate Knee Pain: A Double-Blind, Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: Collagen disruption is one of the underlying causes of knee pain in patients with osteoarthritis and/or diverse cartilage defects. Atelocollagen is a type of collagen that lacks telopeptides and thus has reduced antigenicity. The intra-articular injection of type I atelocollagen supplements collagen levels in the disrupted articular cartilage. This randomized controlled trial evaluated the effects of the intra-articular injection of atelocollagen for the management of knee pain. DESIGN: Two hundred patients with osteoarthritis, chondromalacia, or other cartilage defects were randomly assigned to receive a 3-mL intra-articular injection of atelocollagen (BioCollagen group) or saline (Placebo group). Clinical improvement was evaluated over a 24-week period using the 100-mm visual analogue scale (VAS), the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), and the 36-item Short-Form Health Survey (SF-36). RESULTS: VAS scores were significantly better in the BioCollagen group as compared with the Placebo group at 24 weeks. More patients in the BioCollagen group reported exceeding 20% and 40% VAS improvements. The WOMAC and SF-36 scores were also significantly improved from baseline after the intra-articular injection of atelocollagen; although, the differences between the BioCollagen and Placebo groups were not significant. There were no unexpected or severe adverse events reported for either group. CONCLUSIONS: The results show that an intra-articular injection of atelocollagen effectively alleviates knee pain, as intended. Therefore, the intra-articular injection of atelocollagen can be considered an alternative solution to controlling knee pain due to osteoarthritis and diverse cartilage defects.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".