Intra-articular hyaluronic acid injections for hip osteoarthritis: a level I systematic review
Bibliographic record
Abstract
PURPOSE: The present systematic review investigated the efficacy of intra-articular hyaluronic acid (HA) viscosupplementation for hip osteoarthritis (OA) in patient-reported outcome measures (PROMs) and whether different molecular weights of HA are associated with different outcomes. METHODS: This study was conducted according to the 2020 PRISMA statement. In January 2025, PubMed, Web of Science, Google Scholar, and Embase were accessed. All the randomised controlled trials (RCTs) evaluating the efficacy of intra-articular HA injections in the hip for OA were included. RESULTS: ) were analysed. Patients receiving high molecular weight (HMW) and low molecular weight (LMW) HA showed significant improvements in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analogue scale (VAS) scores (P < 0.05). No significant differences in VAS or WOMAC were observed among groups at 3-4 months of follow-up. However, at 4-6 months, the HMW HA group exhibited significantly lower VAS scores compared to the medium molecular weight (MMW) (mean difference, MD - 1.4, 95% CI - 2.1 to - 0.7, P < 0.0001), placebo (MD - 1.6, 95% CI - 2.1 to - 1.1, P < 0.0001), and control (MD - 1.3, 95% CI - 1.8 to - 0.8, P < 0.0001) groups. WOMAC scores at 4-6 months demonstrated that both HMW and MMW HA performed better than the control group (P < 0.0001), but no significant difference was observed between HMW and MMW (P = 1.0). CONCLUSION: Intra-articular injections of HA effectively reduce knee OA symptoms. Moreover, HMW HA performs better than MMW HA at a mean of 4-6 months of follow-up. LEVEL OF EVIDENCE: Level I, systematic review of RCTs.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".