A clinical pilot study to evaluate the efficacy of oral intake of phellinus linteus (sanghuang) extract on knee joint and articular cartilage
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (KOA) is the most common form of degenerative arthritis. We used Phellinus linteus (PL), which has been well-known anti-inflammatory function. In this study, we will evaluate if PL extract improves symptoms with KOA. METHODS: This study will be an 8-week single-center randomized controlled double-blind clinical trial. Total of 24 subjects with KOA will be enrolled and they will be divided into 3 groups, PL 1,000 mg, PL 1,500 mg and placebo. Subjects will be followed up every 4 weeks with efficacy and safety at the 2nd and 3rd visits. All subjects should maintain a dosage schedule for this protocol. The primary outcome will be assessed with the Korean version of the Western Ontario and McMasters Universities. And the secondary outcomes will be measured using the visual analog scale, quality of life scale (EQ-5D-3L), ESR, C-reactive protein, and C-telopeptide of type-II collagen. Statistical analysis will be performed on the principle of full analysis set. DISCUSSION: This study has inclusion and exclusion criteria and a well-controlled intervention. This clinical trial is the first step to assess the efficacy and safety of PL in patients with KOA. This study will make an important contribution to the literature and aid follow-up research into the use of PL in KOA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".