High-Dose Neuromodulators: A Roundtable on Making Sense of the Data in Real-World Clinical Practice
Bibliographic record
Abstract
BACKGROUND: For aesthetic treatment with botulinum neurotoxin type A (BoNTA), interest in maximizing treatment duration and efficacy has prompted study of doses higher than those used in registration studies. As data emerge, it is important that physicians understand how to apply study findings to their own practice so that patient demand is satisfied. OBJECTIVES: To bring together leading experts in neuromodulators for a roundtable discussion on the implications of high-dose BoNTA studies for patient care. METHODS: The authors reviewed and discussed recent data from high-dose BoNTA studies for abobotulinum toxin A, incobotulinum toxin A, and Oonobotulinumtoxin A. RESULTS: Discussion focused on the challenges of data interpretation and extrapolation of study findings for real-world patient care. The authors participated in a candid discussion of whether the observed improvements in treatment duration and patient satisfaction warrant treatment with high-dose regimens delivered as high-concentration injections. Safety was also discussed, as well as economic considerations for both practices and patients. Of note, for BoNTA products, the registration dose, when administered in a smaller total volume, appears to give rise to more durable results than those observed in pivotal trials, implicating product concentration as an important consideration. Importantly, at higher doses, extended duration of effect does not appear to be at the expense of natural-looking results. CONCLUSIONS: While the authors provide considerations for the development of individual clinical practice, there is no one-size-fits-all recommendation. It may be that "high-dose" BoNTA is in reality the optimal dose; however, important economic considerations may prevent rapid uptake for all patients.
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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 | 0.484 | 0.581 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.043 | 0.070 |
| Open science | 0.014 | 0.029 |
| Research integrity | 0.053 | 0.087 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".