Efficacy and Safety of Intra-articular Injections of Hyaluronic Acid Combined With Polydeoxyribonucleotide in the Treatment of Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical efficacy and safety of intra-articular injection of hyaluronic acid (HA) combined with polydeoxyribonucleotide (PDRN) in patients with knee osteoarthritis in comparison with that of HA alone. METHODS: The current single-center, prospective, randomized, double-blind, controlled study was conducted in 36 patients with knee osteoarthritis at our medical institution. All the eligible patients (n=30) were equally assigned to two treatment arms (trial group 'HA+PDRN' and control group 'HA'). For efficacy assessment, the patients were evaluated for the visual analogue scale (VAS) scores, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Knee Society Scores (KSS), all of which served as efficacy outcome measures. We monitored time-dependent changes in efficacy outcome measures at baseline and 1, 3 and 6 months. Subsequently, we compared differences in changes in efficacy outcome measures at 6 months from baseline between the two groups. Moreover, we assessed the safety based on the treatment-emergent adverse events (TEAEs), adverse drug reactions (ADRs) and any other complications serving as safety outcome measures. RESULTS: There were significant differences in changes in the VAS scores, the WOMAC scores in all domains, except 'Stiffness', the total WOMAC scores, and the KSS scores in all the domains at 6 months from baseline between the two groups (p<0.05). In our series, there were no TEAEs, ADRs, and any other complications. CONCLUSION: Intra-articular injections of HA combined with PDRN can also be considered in the treatment of knee osteoarthritis. However, further large-scale and multi-center studies are required to demonstrate the potential of the proposed combination.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".