A Feline-Specific Anti-Nerve Growth Factor Antibody Improves Mobility in Cats with Degenerative Joint Disease–Associated Pain: A Pilot Proof of Concept Study
Bibliographic record
Abstract
BACKGROUND: Neutralizing antibodies against nerve growth factor (NGF) are analgesic in rodent models, naturally occurring degenerative joint disease (DJD) pain in dogs, and chronic pain in humans. OBJECTIVES: To evaluate the efficacy of a fully felinized anti-NGF antibody (NV-02) for the treatment of DJD pain and mobility impairment in cats. ANIMALS: Thirty-four client-owned cats with DJD-associated pain and mobility impairment. METHODS: In a placebo-controlled, pilot, masked clinical study, cats were randomized to a single treatment with NV-02 (0.4 mg/kg SC [n = 11] or 0.8 mg/kg SC [n = 12]) or placebo (saline, SC [n = 11]). Activity was measured objectively. Additionally, owners completed clinical metrology instruments (client-specific outcome measures [CSOM] and feline musculoskeletal pain index [FMPI]) on days 0 (screening), 14 (baseline), 35, 56, and 77. A repeated-measures model was used to evaluate the objective activity data. RESULTS: NV-02 significantly increased objectively measured activity overall (P = .017) and at 2 (P = .035), 3 (P = .007), 4 (P = .006), 5 (P = .007), and 6 (P = .017) weeks after treatment. CSOM scores (P = .035) and pain (P = .024) showed a significant effect of treatment 3 weeks after administration. In the treatment group, 83% of the owners correctly identified the treatment administered compared with 45% of owners in the placebo group (P = .013). No treatment-related adverse effects were identified. CONCLUSIONS: These pilot data demonstrate a 6-week duration positive analgesic effect of this fully felinized anti-NGF antibody in cats suffering from DJD-associated pain.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".