Efficacy and Safety of Intra-Articular Botulinum Toxin A Injection for Knee Osteoarthritis
Bibliographic record
Abstract
Background: Botulinum toxin A has the potential to be used for analgesia because of its anti-inflammatory effect. The utility of intra-articular injections of botulinum toxin A for knee osteoarthritis remains unclear. The aim of this study was to analyze the utility of such injections in knees with osteoarthritis. Methods: We conducted a literature search of 4 databases (Scopus, PubMed, ClinicalTrials.gov, and Europe PMC) up to September 10, 2022, using formulated keywords. Articles were included in the study if they had data on botulinum toxin A injection compared with the control group in patients with osteoarthritis of the knee. Results were summarized using the standardized mean difference (SMD) and accompanying 95% confidence interval (CI). Results: Pooled analysis of data from 6 trials involving 446 patients with knee osteoarthritis revealed that, compared with placebo, intra-articular injection of botulinum toxin A was associated with greater reductions in early visual analog scale (VAS) pain (SMD, −0.63 [95% CI, −1.08 to −0.18], p = 0.007, I 2 = 79%), late VAS pain (SMD, −0.57 [95% CI, −1.07 to −0.08], p = 0.02, I 2 = 81%), early Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) (SMD, −0.84 [95% CI, −1.61 to −0.06], p = 0.03, I 2 = 90%), and late WOMAC (SMD, −1.12 [95% CI, −1.91 to −0.32], p = 0.006, I 2 = 93%) scores from baseline in patients with knee osteoarthritis. Conclusions: Intra-articular injection of botulinum toxin A may offer benefits in reducing pain and improving function in patients with knee osteoarthritis, with a relatively good safety profile. Larger randomized trials are warranted to confirm the results of our study. Level of Evidence: Therapeutic Level I . See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".