Bibliographic record
Abstract
We have been dedicated to this Special Issue of Dermatologic Surgery on Neuromodulators for the last year. We are excited to present it to our colleagues. What began with potentially unrealistic goals and deadlines turned out to be an inclusive state of the art journal that will hopefully be a reference source for those who are new to the use of toxins and those who already have vast experience. With topics such as nonfacial indications and emerging techniques, we identified practice areas that, although non-FDA–approved, were novel and would be educational to those physicians looking to expand their scope of practice. We also endeavored to find subjects that were diverse and meet the needs of a growing patient population. We have included manuscripts on the basic science of botulinum toxin, anatomical considerations, and potential adverse events. Certainly, any robust publication should have material that will challenge and pique the reader's curiosity, so we included topics such as combination therapy, toxins on the horizon, accessory proteins, and the potential of reversing the effects of neuromodulators. We were fortunate that most of the toxin manufacturers were able to support this issue and contribute, allowing for unbiased scientific rigor. We are very grateful to each of the authors. They were carefully chosen from various specialties and from around the globe and share a wealth of knowledge. We are also thankful for the indefatigable efforts of the staff at the publisher, Wolters Kluwer, especially Marie Edwards. Dr. Bill Coleman (our Editor in Chief) and Barbara Tregre (Managing Editor) gave us daily feedback and helped us allmeet deadlines. This has really been an ideal “dream team,” and we look forward to further collaborations.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.101 | 0.064 |
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".