P108 Anti-nerve growth factor (anti-NGF) antibodies as analgesia in the management of osteoarthritis
Bibliographic record
Abstract
Abstract Background/Aims Osteoarthritis is a major cause of morbidity and disability. Much of this comes from joint pain, which is exacerbated by movement and exercise. Pharmacological analgesia therefore not only has the obvious benefit of alleviating pain, but in doing so, it also facilitates exercise (a pillar of conservative management). Non-steroidal anti-inflammatory drugs (NSAIDs) and opioids are currently the main analgesics used in this context. However, these agents can cause unwanted side effects and are contraindicated in some patients. We thus conducted a systematic review and meta-analysis using the Cochrane collaboration criteria to evaluate the efficacy of anti-nerve growth factor (anti-NGF) antibodies as potential alternative analgesics in osteoarthritis of the hip and/or knee. Whilst tanezumab has been studied extensively and monoclonal anti-NGF antibodies have been reviewed in other pain states, this is the first systematic review of three key anti-NGF antibodies: tanezumab, fulranumab and fasinumab in symptomatic hip and/or knee osteoarthritis. Methods An interdisciplinary work group conducted a literature search across seven electronic databases for the use of anti-NGF antibodies in osteoarthritis. All hip/knee osteoarthritis studies investigating anti-NGF antibodies regardless of dose regimen or phase of trial were included. Studies in which participants received NSAIDs or analgesics other than anti-NGF antibodies, or studies in which the only intervention was the administration of anti-NGF antibodies in combination with NSAIDs or other analgesics were excluded. The Jadad Scale score was used to assess the quality of each study. Results Thirteen studies involving 8,145 patients with a diagnosis of hip and/or knee osteoarthritis were analysed. Demographic information including duration of disease and Kellgren-Lawrence grades were also extracted. Anti-NGF antibodies showed significant improvements compared to placebo as rated on the Western Ontario and McMaster Universities Arthritis Index (WOMAC) scales for pain (SMD= -0.50, 95% CI -0.71 to -0.28, P < 0.00001; I2 = 88%), physical function (SMD= -0.82, 95% CI -1.09 to -0.55, P < 0.00001; I2 = 94%) and stiffness (SMD= -0.88, 95% CI -1.22 to -0.54, P < 0.00001; I2 = 95%). These agents were not associated with a significant increase in serious adverse events but were associated with a significant increase in discontinuation due to adverse events, abnormal peripheral sensations and peripheral neuropathy. Conclusion Anti-NGF antibodies appear very promising with regard to alleviating osteoarthritic hip/knee pain but more studies are needed to determine the optimal dosage and the overall risk-to benefit ratio, particularly with long-term use. Disclosure J. Rammanohar: None. J. Sutton: None. K. Seah: None. W.S. Khan: None. K. To: None.
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".