Bibliographic record
Abstract
BACKGROUND: Botulinum toxin A (BoNT-A) is widely used in treating dystonia and spasticity to managing chronic migraine and cosmetic applications. However, its immunogenic potential presents challenges, such as the development of neutralizing antibodies that lead to diminished therapeutic efficacy over time, known as secondary nonresponse. OBJECTIVE: This review aims to bridge the knowledge gap regarding the immunogenic mechanisms of BoNT-A and to explore effective management strategies to mitigate these immune responses. MATERIALS AND METHODS: The authors conducted a systematic search in databases including PubMed, Embase, and Web of Science, using keywords related to BoNT-A's immunogenicity. The selection process refined 157 initial articles down to 23 relevant studies, which underwent analysis to investigate the underlying mechanisms of immunogenicity and the factors influencing it. RESULTS: The analysis revealed that both the neurotoxin component and the neurotoxin-associated proteins could elicit an immune response. However, only antibodies against the core toxin influence therapeutic outcomes. Various patient-specific factors such as genetic predispositions and prior immune experiences, along with treatment-related factors such as dosage and frequency, play crucial roles in shaping these responses. CONCLUSION: Understanding the specific immunogenic triggers and responses to BoNT-A is critical for optimizing treatment protocols and improving patient outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".