The effect of probiotic Lactobacillus casei Shirota on knee osteoarthritis: a randomised double-blind, placebo-controlled clinical trial
Bibliographic record
Abstract
Knee osteoarthritis (OA) treatment is challenging due to inefficacy and adverse effects of current medications. Probiotic treatment has been shown to promote bone metabolism, reduce pain and inflammatory responses of age-related musculoskeletal disorders, including OA. We aimed to investigate the effect of probiotic Lactobacillus casei Shirota (LcS) on patients with knee OA. 537 patients with knee OA were enrolled in this double-blind, placebo-controlled trial, who were randomised to receive skimmed milk containing either LcS or placebo daily for 6 months. Primary outcome was defined as changes in WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) and VAS (visual analog scale) scores. Secondary outcome was defined as changes in serum levels of high sensitivity C-reactive protein (hs-CRP). After 6 months of treatment, both WOMAC and VAS scores were significantly improved in the LcS groups of patients compared to the placebo group. Serum levels of hs-CRP were also significantly lower in patients receiving LcS than placebo. Strong linear correlations were observed between serum hs-CRP levels and WOMAC and VAS scores. LcS consumption could serve as a novel therapeutic option in the clinical management of knee OA, improving treatment outcome likely through reducing serum hs-CRP levels.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".