The effects of lemon verbena extract and hydrolyzed collagen supplementation, either alone or in combination, on subjects with mild arthritis of the knee joints
Bibliographic record
Abstract
Background Extracts of lemon verbena have anti‐inflammatory and anti‐oxidant properties and may represent a safe nutritional alternative to improve joint health and mobility in subjects with osteoarthritis. Previous research has shown that the administration of 10 grams of collagen hydrolysate per day for at least 3 months has a beneficial impact on pain symptoms and joint function. This study examined the impact of administering low‐dose hydrolyzed collagen (HC), standardized lemon verbena extract (LV), and a combination of both ingredients (LV+HC) on joint health in individuals with osteoarthritis of the knee. Methods A double‐blind, placebo‐controlled, randomized human study in volunteers with mild arthritis of the knee joints was performed. Subjects either received 5 g of HC (Fortigel, Gelita AG, Eberbach, Germany, n = 20), 1 g of LV (Lemon verbena extract, standardized to 10% anoxymins, n = 18), 1 g of LV plus 5 g of HC (LV+HC, n = 20) or placebo (PLA, 5 g microcrystalline cellulose, n = 17) for a period of six month. Treatment effects on perceived pain and functionality were evaluated at weeks 0 (pre‐test), 3, 6, 9, 12, 15, 18 and 24 (post‐test) using the Lequesne index, in addition to a weekly assessment by the WOMAC Index. Results The Lequesne score‐difference index showed faster significant improvements (p<0.05) in the LV and LV+HC groups (week 6) in comparison to HC (week 9) and PLA (week 18) and higher individual responder rates (defined as score difference ≥3.5 compared to baseline), reaching 70% at the end of study for the LV+HC group, compared to 38% for LV and 64% for HC. The LV+HC group showed greater improvements in mobility and knee‐function in comparison to the individual ingredients (LV+HC 37%, LV 26%, HC 28%). The faster onset of beneficial effects in the LV+HC group was confirmed by the WOMAC index. Conclusion: Supplementation of HC, LV or a combination of LV and HC can significantly improve parameters of pain and functionality in subjects with mild arthritis of knee joint. Combining HC with an anti‐inflammatory ingredient might increase the responder rate and decrease the time needed for the onset of the beneficial effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".