Bibliographic record
Abstract
Background: It is generally agreed that diffusion of botulinum toxin occurs, but the extent of the spread and its clinical importance are disputed. Many factors have been suggested to play a role but which have the most clinical relevance is a subject of much discussion. Methods: This review discusses the variables affecting diffusion, including protein composition and molecular size as well as injection factors (e.g., volume, dose, injection method). It also discusses data on diffusion from comparative studies in animal models and human clinical trials that illustrate differences between the available botulinum toxin products (onabotulinumtoxinA, abobotulinumtoxinA, incobotulinumtoxinA, and rimabotulinumtoxinB). Results: Neither molecular weight nor the presence of complexing proteins appears to affect diffusion; however, injection volume, concentration, and dose all play roles and are modifiable. Both animal and human studies show that botulinum toxin products are not interchangeable, and that some products are associated with greater diffusion and higher rates of diffusion-related adverse events than others. Discussion: Each of the botulinum toxins is a unique pharmacologic entity. A working knowledge of the different serotypes is essential to avoid unwanted diffusion-related adverse events. In addition, clinicians should be aware that the factors influencing diffusion may range from properties intrinsic to the drug to accurate muscle selection as well as dilution, volume, and dose injected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".