Glucosamine Sulfate Efficacy in Treating Knee Osteoarthritis: A Follow-Up Study
Bibliographic record
Abstract
Osteoarthritis (OA) can be treated using either a pharmacological or non-pharmacological approach, or a combination of both. The purpose of the present study was to investigate the efficacy of crystalline glucosamine sulfate (CGS) in patients with knee OA. This open-label prospective study (with a 12-month follow-up) included 111 patients of both genders suffering from knee OA, who attended the Special Hospital for Rheumatic Diseases in Novi Sad, Serbia during the 2011-2013 period. Patients were divided into the experimental (n=52) and the control (n=59) group. While the former was prescribed CGS 1500 mg/day, the latter was treated with nonsteroidal anti-inflammatory drugs (NSAIDs) according to the standard protocol. The efficacy of both treatment modes was assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Lequesne index, along with the radiological findings which involved knee joint space width (JSW) measurements. One year following the initial assessment, all patients reported pain intensity reduction; however, those in the CGS group experienced significantly lower pain intensity when compared with controls. At the end of the study, no reduction in the progression of joint structure damage (p>0.5) was noted in either group. Thus, while CGS demonstrated symptomatic efficacy, it failed to delay the progression of knee OA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".