Efficacy and Safety of Anti–Nerve Growth Factor Antibody Therapy for Hip and Knee Osteoarthritis: A Meta-analysis
Bibliographic record
Abstract
Background: The efficacy and safety of anti–nerve growth factor (NGF) antibody therapy used for osteoarthritis (OA) pain are controversial. Purpose: To evaluate the efficacy and safety of anti-NGF antibody therapy via a meta-analysis of randomized controlled trials (RCTs). Study Design: Systematic review; Level of evidence, 1. Methods: PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science databases were searched for RCTs assessing anti-NGF antibody treatments for hip and knee OA. A total of 623 records were retrieved from the databases. A random-effects model was used to assess primary and secondary outcomes. Bias was assessed using the Cochrane Collaboration tool, funnel plots, and the Egger test. Subgroup analyses were used to assess the efficacy and safety of the independent variables. Sensitivity analysis was conducted to evaluate the effectiveness of tanezumab and the effectiveness of anti-NGF antibodies compared to active comparator drugs. We present the effects of dose, administration mode, and treatment duration on the efficacy and safety of anti-NGF antibody therapy. Results: There were 19 RCTs included in our meta-analysis. Anti-NGF antibody treatment showed significant improvements on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for pain, physical function, and stiffness as well as on a patient global assessment (PGA). The overall standardized mean differences were as follows: WOMAC pain (–0.31 [95% CI, –0.36 to –0.26]; Z = 11.75; P < .001; I 2 = 38%), WOMAC physical function (−0.36 [95% CI, –0.41 to –0.30]; Z = 12.67; P < .001; I 2 = 44%), WOMAC stiffness (–3.59 [95% CI, –4.87 to –2.30]; Z = 5.47; P < .001; I 2 = 98%), and PGA (−0.28 [95% CI, –0.34 to –0.22]; Z = 9.39; P < .001; I 2 = 50%). Anti-NGF antibody treatment resulted in a greater incidence of adverse events (risk ratio, 1.09 [95% CI, 1.06 to 1.12]; Z = 5.60; P < .001; I 2 = 0%). The incidence of serious adverse events was similar between the treatment and control groups (risk ratio, 1.15 [95% CI, 0.98 to 1.34]; Z = 1.71; P = .09; I 2 = 0%). Conclusion: Anti-NGF antibody treatment significantly relieved pain and improved function in patients with hip and knee OA. However, no conclusion could be drawn regarding the optimal treatment plan for anti-NGF antibodies when all 3 variables (dose, administration mode, and treatment duration) were combined in the analyses.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.078 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".