The Efficacy of Nerve Growth Factor Antibody for the Treatment of Osteoarthritis Pain and Chronic Low-Back Pain: A Meta-Analysis
Bibliographic record
Abstract
Background: Nerve growth factor (NGF) plays a crucial role in pain modulation, it is considered as a new therapeutic target for pain therapy. The purpose of this meta-analysis was to study the efficacy of anti-NGF antibody for the treatment of osteoarthritis pain and chronic low-back pain and to provide evidence and hints for further research and practice. Methods: PubMed, Embase, Wanfang Data, and China National Knowledge Infrastructure (CNKI) were searched from inception to November 30, 2019. Eligible studies should include randomized clinical trials-based investigations of anti-NGF antibody treatment for osteoarthritis pain and chronic low-back pain. Pooled overall mean changes from baseline to check point in the Western Ontario and McMaster Universities Arthritis Index (WOMAC) measures of pain, physical function and Patient’s Global Assessment (PGA) were calculated with either a fixed-effects model or a random-effects model depending on the tests for heterogeneity. Sensitivity analysis and bias of publication were assessed. Results: A total of 7 studies (3890 patients) were included in this meta-analysis. The pooled analysis showed statistically significant reduction in the WOMAC pain (standardized mean difference (SMD) = -2.22, 95% confidence interval (CI) = -3.44 to -0.99, Z = -3.55, P = 0.0004; I2 = 99%), the WOMAC Physical Function (SMD = -2.76, 95% CI = -4.22 to -1.30, Z = -3.71, P = 0.0002; I2 = 99%), and the PGA Index (SMD = -2.76, 95% CI = -4.42 to -1.09, Z = -3.24, P = 0.0012; I2 = 99%), respectively. Pooled difference of adverse events rates in experimental and control groups was 0.11 (95% CI = 0.02 to 0.20, Z = 2.41, P = 0.016; I2 = 83%). Conclusion: Our meta-analysis data indicate that anti-NGF antibody can relive pain and improve function in patients with osteoarthritis pain and chronic low-back pain. Keywords: anti-NGF antibody, osteoarthritis pain, chronic low-back pain, immunological treatment, meta-analysis
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.072 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".