Handling Botulinum Toxins
Bibliographic record
Abstract
BACKGROUND Botulinum toxin (BoNT) has been in use since the late 1970s, and over the last 20 years, its use has been extended to new indications in various areas of medicine. During these years of clinical use, some of the initial ideas have changed, and others have remained stable along with increasing experience with and knowledge about BoNTs. OBJECTIVE To review the literature and prescribing information on all of the available products and to update the concept of handling toxins (preparations, reconstitution, storage, sterility, and dilution). METHODS A review (not Cochrane type analysis) of the medical literature based on relevant databases (MEDLINE, PubMed, Cochrane Library, specialist textbooks, and manufacturer information) was performed. CONCLUSIONS Many of the precautions around BoNT use, often recommended by the manufacturers, are described in the clinical literature as too restrictive. The literature suggests that toxins may be sturdier and more-resistant to degradation than previously understood. Dr. Ada R. Trindade de Almeida has been a consultant to Allergan, Inc. and participated in clinical trials for Allergan and Galderma. Dr. Alastair Carruthers is a consultant to Allergan, Inc. and Merz GmbH and has been paid to do research for both companies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".