Efficacy of glucosamine sulfate in lowering serum level of interleukin-1β in symptomatic primary knee osteoarthritis: Clinical and laboratory study
Bibliographic record
Abstract
ObjectiveTo identify the effect of α-d glucosamine sulfate (GS) on serum level of interleukin-1β (IL-1β) in patients with symptomatic primary knee OA.MethodsSixty patients (mean age = 52.2 ± 8.6 years), fulfilling the American College of Rheumatology criteria of idiopathic knee OA, were randomized to receive either 1500 mg α-d GS and 1200 mg Ibuprofen (group I), or only 1200 mg Ibuprofen (group II) daily for 12 weeks. Patients were followed up by the Visual Analogue knee pain Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) functional index and quantitative detection of IL-1β serum levels. Reference serum level of IL-1β was determined in 20 matched healthy volunteers.ResultsGroup I showed significant progressive improvement in pain VAS and total WOMAC scale, pain, stiffness and function subscales during the follow up visits compared to group II. At baseline, both groups had significantly higher IL-1β serum level than the control group. On follow up group I showed significant progressive reduction in IL-1β serum level with a final level that was significantly lower than group II and was not significantly higher than the control group. In group II the reductions in IL-1β serum level did not reach the level of statistical significance and the final level persisted significantly higher than that of the control group.ConclusionAdding α-d GS to treatment of primary symptomatic knee OA could relieve symptoms, improve function and affect some of the disease mechanisms.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".