Oral intake of Streptococcus thermophilus improves knee osteoarthritis degeneration: A randomized, double-blind, placebo-controlled clinical study
Bibliographic record
Abstract
) is a newly founded bacterium from human breast milk, and it is able to produce hyaluronate (HA) in gastrointestinal (GI) tract. A recent study has proved that TCI633 can substantially alleviate synovial tissue inflammation and cartilage damage in the animal models, but so far it has never been applied in clinical intervention. In this study, we recruited 80 subjects and conducted 12 weeks clinical trial to validate the efficacy of TCI633 for improvement of the progression of KOA. TCI633 could improve serum collagen type II C-telopeptide (sCTX-II) and serum C-reactive protein (sCRP) by 41.58% and 39.58%, respectively, after the study. The improvement rates for sCTX-II and sCRP in TCI633 group were 54% and 57%, respectively, at 12 weeks. Compared to the results of placebo, the indistinct improvement progresses of sCTX-II and sCRP might be caused by the uneventful distribution of K/L populations between the TCI633 and placebo groups, a short term of study period, and few recruited subjects. Moreover, the results of Western Ontario and McMaster Universities (WOMAC) questionnaires show that TCI633 might retard the progression and development of KOA after the trial. In brief, this preliminary research may provide an alternative approach to the improvement of KOA by probiotics although more detailed investigations should be conducted for solid conclusions.
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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 | 0.001 |
| 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 | 0.003 | 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".