Single-Dose IncobotulinumtoxinA in the Treatment of Early-Stage Knee Osteoarthritis: Results from a Preliminary Single-Arm Clinical Trial
Bibliographic record
Abstract
Osteoarthritis (OA) is the most prevalent rheumatologic disease and a leading cause of years lived with disability worldwide. There are no disease-modifying drugs available to treat it. This study aimed to evaluate the effect of a single dose of 100U botulinum neurotoxin-A (BoNT-A) in patients with early knee OA. We designed a single-arm preliminary clinical trial in patients diagnosed with knee OA (KOA) grades I and II. 45 Patients received a single dose of 100U IncobotulinumtoxinA in the retro-patellar bursa and received nutritional and physical rehabilitation indications. Patients were evaluated at baseline and at days 5, 30, 60, and 90 after injection. The primary outcome was the reduction in pain using the visual analog scale (VAS). Knee function was evaluated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). We assessed secondary adverse effects and measured muscular strength in every consultation. Descriptive endpoint summaries and a generalized linear random-effect model were used to evaluate changes in each follow-up time compared to baseline. IncobotulinumtoxinA treatment significantly (p < 0.001) reduced pain in all treated patients at day 90 compared to day 0. Patients showed a significant reduction in total WOMAC score (p < 0.001), from a mean baseline of 44.6 (95% CI; 41.4, 47.8) to 4.4 at day 90 (95% CI; 0.2, 0.3). Our results show that IncobotulinumtoxinA applied in the retro-patellar bursa is a safe and effective treatment for pain in patients with early-stage KOA, offering a potential alternative for symptomatic control in KOA.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".