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Record W3112591201 · doi:10.12968/joan.2020.9.10.414

The uses of botulinum toxin A in facial aesthetics

2020· article· en· W3112591201 on OpenAlexaff
Gemma Fromage

Bibliographic record

VenueJournal of Aesthetic Nursing · 2020
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsSKiN Health
Fundersnot available
KeywordsBotulinum toxinForeheadGlabellaMedicineFacial musclesFacial rejuvenationHyperhidrosisAnatomySurgery

Abstract

fetched live from OpenAlex

The demand for non-surgical facial rejuvenation procedures is rising, and they are more popular than ever, with the aesthetic uses of botulinum toxin dramatically changing the landscape of facial rejuvenation. Botulinum toxin is a neurotoxin that works within cholinergic synapses present at neuromuscular endplates, preventing the transmission of neurotransmitters, such as acetylcholine, from nerves to muscles. This interference with nerve impulses leads to the muscles being temporarily weakened (paralysis). Botulinum toxin A was approved by the US Food and Drug Administration (FDA) for use in the glabella in 2002, followed by crow's feet in 2013 and then the forehead in 2017, with other aesthetic uses being classed as off-license. Botulinum toxin A yields good results in carefully selected patients, and a thorough consultation should always take place. Consultations should include management of expectations and the explanation that botulinum toxin A works on dynamic lines, rather than static lines. Treatment areas can be split into the upper face (glabellar, transverse forehead lines and lateral orbicularis oculi); mid face (bunny lines and perioral vertical lip lines); and lower face (masseter hypertrophy, mentalis, platysmal bands and gummy smile). Each patient should be assessed individually to determine individual anatomy, including the size, strength and location of muscles, with doses being adjusted accordingly.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 designOther design
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

Citations1
Published2020
Admission routes1
Has abstractyes

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