The Effectiveness of Anti-Nerve Growth Factor Monoclonal Antibodies in the Management of Pain in Osteoarthritis of the Hip and Knee: A PRISMA Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review and meta-analysis of the efficacy of anti-nerve growth factor (NGF) monoclonal antibodies in osteoarthritis pain (hip and knee). DESIGN: Grade the evidence for anti-NGF use. METHODS: An interdisciplinary work group conducted a literature search for anti-NGF use in osteoarthritis. The systematic review was performed in accordance with methods described by the Cochrane collaboration. General inclusion criteria included all osteoarthritis trials studying any monoclonal anti-NGF antibody at any dose/phase. Excluded studies were those where participants received NSAIDs or analgesics other than anti-NGF antibodies. The Jadad Scale score was used to assess the quality of the included studies. RESULTS: Thirteen studies were included in the analysis, involving 8145 participants with a diagnosis of hip and/or knee osteoarthritis. Anti-NGF antibody treatment was associated with a significant improvement in all Western Ontario and McMaster Universities Arthritis Index (WOMAC) indices when compared to placebo. These agents were not associated with a significantly increased incidence of serious adverse events but were associated with significant increases in therapy discontinuation due to adverse events or side effects (e.g., peripheral neuropathy). CONCLUSIONS: Future randomized clinical trials are needed to characterize the overall risk-to-benefit ratio of anti-NGF antibodies in managing pain associated with OA, particularly with long-term use, in order to verify their efficacy and safety in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".