Clinical Efficacy of Glucosamine plus Sodium Hyaluronate for Osteoporosis Complicated by Knee Osteoarthritis and Its Influence on Joint Function and Bone Metabolic Markers
Bibliographic record
Abstract
Background: Osteoporosis (OP) associated with knee osteoarthritis (KOA) is common in older men and postmenopausal women, and it is important to find reliable and effective treatments for this disease to improve joint function and bone metabolism in this population. Objective: To clarify the clinical efficacy of glucosamine (GlcN) plus sodium hyaluronate (SH) for OP complicated by KOA (OP + KOA) and its influence on joint function and bone metabolic markers (BMMs). Methods: Admitted from July 2019 to July 2021, 126 patients with OP + KOA were selected, including 76 cases (observation group) treated with GlcN plus SH and 50 cases (control group) given GlcN alone. The pain, joint function, BMMs, and clinical efficacy were evaluated and compared. Pain and joint function assessments employed the Visual Analogue Scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) plus Lysholm Knee Scoring Scale, respectively. BMMs mainly measured bone gla protein (BGP), serum tartrate-resistant acid phosphatase variant (TRACP)-5b, type I collagen cross-linked C-telopeptide (CTX-1), and bone-specific alkaline phosphatase (BALP). Results: Higher posttreatment VAS scores were determined in observation group as compared to control group; observation group showed lower WOMAC scores of joint function and higher Lysholm scores than control group; in terms of BMMs, TRACP-5b and CTX-1 were lower while BGP and BALP were higher in observation group; the curative effect was also higher in observation group. All the above differences were statistically significant. Conclusions: GlcN plus SH has definite clinical efficacy in the treatment of OP + KOA, which can not only significantly improve patients' joint function and bone metabolism but also relieve pain, with high clinical popularization value.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".