Efficacy of GCWB106 (Chrysanthemum zawadskii var. latilobum extract) in osteoarthritis of the knee
Bibliographic record
Abstract
BACKGROUND: GreenCross Wellbeing Corporation (GCWB) 106 is a food item based on Chrysanthemum zawadskii var. latilobum extract. It has an inhibitory effect on joint inflammation. OBJECTIVE: This study investigated the efficacy and safety of GCWB106 for osteoarthritis (OA) of the knee joint. METHODS: Overall, 121 participants with mild OA were recruited and randomly divided into two groups. One group received GCWB106 for 12 weeks and the other group received placebo for 12 weeks. Outcomes were evaluated using the Korean-Western Ontario and McMaster Universities Index (K-WOMAC), visual analog scale, Korean Short Form Health Survey 36 score, and laboratory test results. RESULTS: After 12 weeks of study treatment, the GCWB106 group exhibited a significant improvement compared with the placebo group in overall K-WOMAC score (P = .042) and K-WOMAC physical function score (P = .015). The GCWB106 group showed significant improvement in the visual analog scale pain score (P < .001) compared with the placebo group after 6 weeks and 12 weeks; no adverse drug reactions or serious adverse events were reported in either group. CONCLUSION: GCWB106 can safely reduce pain and improve knee function with therapeutic effects in OA of the knee joint. LEVEL OF EVIDENCE: Randomized, double-blind, placebo-controlled clinical study, Level I.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".