Meta‐analysis of neutralizing antibody conversion with onabotulinumtoxinA (BOTOX®) across multiple indications
Bibliographic record
Abstract
This meta-analysis evaluated the frequency of neutralizing antibody (nAb) conversion with onabotulinumtoxinA (BOTOX®; Allergan) across five studied indications. The analysis was based on large, controlled or prospective, open-label trials (durations 4 months to ≥2 years). Serum samples were analyzed for nAbs using the Mouse Protection Assay. Subjects who were antibody negative at baseline and had at least one analyzable postbaseline antibody assay result were included. The 16 clinical studies included 3,006 subjects; of these, 2,240 met the inclusion criteria for this analysis. Subjects received 1-15 treatments (mean 3.8 treatments) with onabotulinumtoxinA. Total doses per treatment cycle ranged from 10 or 20 units in glabellar lines to 20-500 units in cervical dystonia. The numbers of subjects who converted from an antibody-negative status at baseline to antibody-positive status at any post-treatment time point were: cervical dystonia 4/312 (1.28%), glabellar lines 2/718 (0.28%), overactive bladder 0/22 (0%), post-stroke spasticity 1/317 (0.32%), and primary axillary hyperhidrosis 4/871 (0.46%). Across all indications, 11/2,240 subjects (0.49%) converted from antibody negative at baseline to positive at one or more post-treatment time points, but only three subjects became clinically unresponsive to onabotulinumtoxinA at some point following a positive assay. Based on these large trials, the frequency of antibody conversion after onabotulinumtoxinA treatment is very low, and infrequently leads to loss of efficacy. © 2010 Movement Disorder Society.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".