Evaluation of clinical efficacy of undenatured type ii collagen in the treatment of osteoarthritis of knee. A randomized controlled study
Bibliographic record
Abstract
Background: One of the leading diseases among the elderly population is Osteoarthritis of Knee, which occurs due to the destruction of articular cartilage by immunologic or mechanical factors. Collagen is the most abundant component of the cartilage. Collagen derivatives have shown to have disease modifying action in osteoarthritis. This study evaluates the ability of Undenatured Type II Collagen to improve knee symptoms in patients diagnosed with Osteoarthritis of knee. Method: A total of 60 patients who satisfied our inclusion and exclusion criteria were randomly distributed in two groups. One treated with 1500 mg/day of acetaminophen (group A; n=30) and the other treated with 10 mg/day of native type II collagen (group UC-II; n=30) for 3 months. Visual Analogue Scale (VAS) at rest and during walking, Western Ontario McMaster (WOMAC) pain and WOMAC function were recorded. Results: After 180 days of treatment, although there was overall reduction in WOMAC groups AC+ UC-II was significantly better than AC for “night pain” (p= 0.040) and “resting pain” (p= 0.025). AC+ UC-II was significantly better than AC for “ascending stairs at 60 days and 180 days” (p=0.020 & 0.035 respectively), “at night while in bed” (p=0.020) at 60 days and difficulty walking on flat surface at 180 days (p=0.036). AC and UC-II was most effective and reduced VAS & WOMAC scores by 30%. Conclusion: The results suggest that undenatured type II collagen treatment combined with acetaminophen has more marked effect when compared to only acetaminophen for symptomatic treatment of patients with knee osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".