Efficacy and safety of intraarticular hyaluronic acid and corticosteroid for knee osteoarthritis: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: A meta analysis to compare efficacy and safety of intraarticular hyaluronic acid (HA) and intraarticular corticosteroids (CS) in patients with knee osteoarthritis. METHOD: Potential studies were searched from the electronic databases included PubMed, Embase, web of science and the Cochrane Library up to August 2016. High quality randomized controlled trials (RCTs) were selected based on inclusion criteria. RevMan 5.3 were used for the meta-analysis. RESULTS: 12 RCTs containing 1794 patients meet the inclusion criteria. Visual analog scale (VAS) score in CS group decrease more than HA group up to 1 month (p = 0.03) and it shows equal efficacy at 3 months (p = 0.29); HA is more effective than CS at 6 months (p = 0.006). To Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, there is no significant difference for two groups at 3 months (p = 0.29); HA shows greater relative effect than CS at 6 months (p = 0.005). No significant difference is found on proportion of rescue medication use after initiation of treatment (p = 0.58) and proportion of withdrawal for knee pain (p = 0.54). HA and CS exhibit equal efficacy on improvement of active range of knee flexion at 3 months (p = 0.73) and 6 months (p = 0.43). More topical adverse effects occurred in intraarticular HA group when compared with intraarticular CS group. CONCLUSION: Intraarticular CS is more effective on pain relief than intraarticular HA in short term (up to 1 month), while HA is more effective in long term (up to 6 months). Two therapies benefit similarly for knee function improvement. Both two methods are relatively safe, but intraarticular HA causes more topical adverse effects compared with intraarticular CS.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.066 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".