Evaluation of the Effectiveness of Hyaluronic Acid Injections in Treatment of Small Joint Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is an incurable, progressive, degenerative and debilitating joint disease, characterized by pain, stiffness and limitation of joint mobility. Treatment is limited to symptom management and includes pharmaco- and physiotherapy, rehabilitation and surgery. OBJECTIVE: To evaluate safety and effectiveness of a selected hyaluronic acid injectable in the treatment of small joint osteoarthritis. MATERIALS AND METHODS: 324 patients (F: 190, M: 134) aged 19-84 completed the study. Each participant received three, weekly intra-articular hyaluronic acid injections (Suplasyn®, Bioniche Life Sciences Inc, Belleville, Ontario; 700 kDa, 7 ml/7 mg). Morning stiffness and pain intensity (at rest and movement-induced) before and after treatment as well as post-treatment improvement in joint mobility were assessed based on questionnaires filled out by patients and their physicians prior to treatment commencement and at its conclusion. Participants used a Numerical Rating Scale (range 0-10 points) to quantify assessments. Data was analysed with chi-square and Fisher's exact tests and p = 0.05 was accepted as significant. RESULTS: A significant (p <0.001) post-treatment reduction in (1) morning stiffness (median NRS score decrease from 6 to 2 points), (2) pain intensity at rest (median NRS score decrease from 5 to 2 points) and movement induced (median NRS value decrease from 6 to 2 points). Improvement in joint mobility was reported by 97% of patients. Treatment tolerability (safety) was assessed as good to excellent by 25 % and 74% of patients, respectively. No serious adverse reactions were reported. CONCLUSION: Hyaluronic acid injections are a highly effective and safe treatment for symptomatic small joint osteoarthritis.
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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) | 0.000 | 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) | 0.001 | 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".