Tanezumab for Patients with Osteoarthritis of the Knee: A Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Tanezumab is a new therapeutic intervention for patients with osteoarthritis (OA) of the knee. We performed the present meta-analysis to appraise the efficacy and safety of tanezumab for patients with knee OA. METHODS: We systematically searched randomized controlled trials from PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL). The primary outcomes were mean change in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, the WOMAC physical function and patient's global assessment (PGA). Outcomes were reported as the standard mean difference (SMD) or relative risk (RR) with 95% confidence interval (CI). We assessed the pooled data using a random-effects model. RESULTS: Of the identified studies, four were eligible and were included in this meta-analysis (N = 1839 participants). Compared with the placebo groups, tanezumab yielded a significant reduction in mean change in the WOMAC pain (SMD = 0.51, 95% CI 0.34 to 0.69, P<0.00001), the WOMAC physical function (SMD = 0.56, 95% CI 0.38 to 0.74, P<0.00001) and PGA (SMD = 0.34, 95% CI 0.22 to 0.47, P<0.00001). There was no significant difference in serious adverse events (RR = 1.06, 95% CI 0.59 to 1.92, P = 0.84) between the tanezumab and placebo groups. Tanezumab significantly increased discontinuations due to adverse events (RR = 2.89, 95% CI 1.59 to 5.26, P = 0.0005), abnormal peripheral sensations (RR = 3.14, 95% CI 2.12 to 4.66, P<0.00001), and peripheral neuropathy (RR = 6.05, 95% CI 2.32 to 15.81, P = 0.0002). CONCLUSION: Tanezumab can alleviate pain and improve function for patients with OA of the knee. However, considering the limited number of studies, this conclusion should be interpreted cautiously and more clinical randomized controlled trials are needed to verify the efficacy and safety of tanezumab for OA of the knee.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".