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Record W4407561586 · doi:10.1097/dss.0000000000004567

The History of Neuromodulators in Dermatologic Surgery

2025· article· en· W4407561586 on OpenAlexaff
Andrea Cespedes Zablah, Seth L. Matarasso, Jean Carruthers

Bibliographic record

VenueDermatologic Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBotulismMedicineFood and drug administrationBotulinum toxinDermatologic surgeryMedical literatureDermatologyIntensive care medicineSurgeryPharmacologyPathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: In 1817 and 1820, the German physician Justinus Kerner published a series of cases of lethal food poisoning that would unknowingly have a lasting impact on medical science. His compilation of over 75 cases linked the consumption of smoked sausages in the small town of Herrenberg in Württemberg, recounted a constellation of symptoms that today the authors call botulism. Now, over 2 centuries later, the discovery, study and refinement of the toxin causing clinical botulism has led to the acceptance of neuromodulators as a treatment for a wide variety of medical concerns. OBJECTIVE: The aim of this article is to understand the many historical advances in the mechanism of action of botulinum neurotoxins, the wide range of indications that are currently available and Dermatologic Surgery 's role in this evolution. MATERIALS AND METHODS: A PubMed retrospective search to identify literature on the history of botulinum toxin was undertaken. RESULTS: Botulinum toxin is the newest therapeutic generational drug with over 30 approved indications in 90 countries. There are now 7 FDA (food and drug administration) approved neuromodulators with several more under current review. CONCLUSIONDermatologic Surgery has had a seminal role in the advancement of neuromodulators and publishing associated literature.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.244
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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