Effect of the Novel Low Molecular Weight Hydrolyzed Chicken Sternal Cartilage Extract, BioCell Collagen, on Improving Osteoarthritis-Related Symptoms: A Randomized, Double-Blind, Placebo-Controlled Trial
Bibliographic record
Abstract
Osteoarthritis (OA) is a significant source of pain and disability. Current medical and surgical treatments can be costly and have serious side effects. The aim of this randomized, double-blind, placebo-controlled trial was to investigate the tolerability and efficacy of BioCell Collagen (BCC), a low molecular weight dietary supplement consisting of hydrolyzed chicken sternal cartilage extract, in the treatment of OA symptoms. Patients (n = 80) in the study had physician-verified evidence of progressive OA in their hip and/or knee joint. Joint pain had been present for 3 months or longer at enrollment, and pain levels were 4 or higher at baseline as assessed by Physician Global Assessment scores. Subjects were divided into two groups and administered either 2 g of BCC or placebo for 70 days. Other outcome measurements included visual analogue scale (VAS) for pain and Western Ontario and McMaster Universities Arthritis Index (WOMAC) scores taken on days 1, 35, and 70. The tolerability profile of the treatment group was comparable to that of the placebo. Intent-to-treat analysis showed that the treatment group, as compared to placebo, had a significant reduction of VAS pain on day 70 (p < 0.001) and of WOMAC scores on both days 35 (p = 0.017) and 70 (p < 0.001). The BCC group experienced a significant improvement in physical activities compared to the placebo group on days 35 (p = 0.007) and 70 (p < 0.001). BCC was well tolerated and found to be effective in managing OA-associated symptoms over the study period, thereby improving patient's activities of daily living. BCC can be considered a potential complement to current OA therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".