Short-term effect of the combination of hyaluronic acid, chondroitin sulfate, and keratin matrix on early symptomatic knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: In the last years, symptomatic slow-acting drugs for osteoarthritis (SYSADOA) have been vastly studied and have generated considerable interest among clinicians. SYSADOA are generally used as a ground therapy with the main rationale to reduce the consumption of nonsteroidal anti-inflammatory drugs (NSAIDs) and thus limit the related adverse events. MATERIAL AND METHODS: In this study, we evaluated the short-term effect of an oral combination of hyaluronic acid, chondroitin sulfate, and keratin matrix on early symptomatic knee osteoarthritis. Forty patients were treated for 1 month and were allowed to assume analgesics or NSAIDs if necessary. RESULTS: At 2 months, the mean reduction of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score was 36% (p<0.001), and the mean reduction of the WOMAC pains score was 40% (p<0.001). Only two patients reported a sporadic need to assume analgesics; no patient reported any side effect during the study period. CONCLUSION: This data demonstrates that the oral combination of hyaluronic acid, chondroitin sulfate, and keratin matrix is safe, well tolerated, and shows a rapid action reducing pain and improving joint function and stiffness in early symptomatic knee osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".