Intra-articular hyaluronic acid injections may be beneficial in patients with less advanced knee osteoarthritis : a systematic review of randomised controlled trials
Bibliographic record
Abstract
Background Knee osteoarthritis is a degenerative joint disease that impairs quality of life. Hyaluronic acid (HA) injections are used to restore synovial fluid viscosity and improve joint function. Objectives The present systematic review investigated the prognostic factors influencing the effect of intra-articular HA injections for knee osteoarthritis (OA). The endpoint of interest was whether patient characteristics and molecular weight of the HA influence patient-reported outcome measures (PROMs) at different follow-ups. Methods This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the 2020 PRISMA statement. All randomised controlled trials (RCTs) investigating the efficacy of intra-articular HA injections in the knee were accessed. Data concerning the visual analogue scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC) and Lequesne scales were collected at baseline and the last follow-up. OA was scored using the Kellgren–Lawrence (KL) classification. The endpoint of interest was whether patient characteristics and the molecular weight of HA influence clinical outcomes. Results The study included 71 RCTs and data from 10,590 patients; 67% (7082 of 10,570) were women. The mean age of the patients was 61.8 ± 5.1 years, and the mean body mass index (BMI) was 27.8 ± 2.3 kg/m2. Conclusions HA injections lead to an initial worsening of symptoms; however, patients with early stage osteoarthritis, particularly older women, may experience significant long-term improvements. Further research should standardise treatment protocols and investigate the role of HA molecular weight in optimising outcomes.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".