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Consensus Recommendations on the Use of Botulinum Toxin Type A in Facial Aesthetics

2004· review· en· W1994409800 on OpenAlexaff
Jean Carruthers, Steven Fagien, Seth L. Matarasso

Bibliographic record

VenuePlastic & Reconstructive Surgery · 2004
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAestheticsBotulinum toxinPsychologyArtNeuroscience

Abstract

fetched live from OpenAlex

The use of botulinum toxin type A for facial enhancement is the most common cosmetic procedure currently undertaken in the United States. Overall clinical and study experience with botulinum toxin type A treatment for facial enhancement has confirmed that it is effective and safe in both the short and long term. Nevertheless, consistent guidelines representing the consensus of experts for aesthetic treatments of areas other than glabellar lines have not been published. Therefore, a panel of experts on the aesthetic uses of Botox Cosmetic (botulinum toxin type A; Allergan, Inc., Irvine, Calif.) was convened to develop consensus guidelines. This publication comprises the recommendations of this panel and provides guidelines on general issues, such as the importance of the aesthetic evaluation and individualization of treatment, reconstitution and handling of the botulinum toxin type A, procedural considerations, dosing and injection-site variables, and patient selection and counseling. In addition, specific considerations and recommendations are provided by treatment area, including glabellar lines, horizontal forehead lines, "crow's feet," "bunny lines" (downward radiating lines on the sides of nose), the perioral area, the dimpled chin, and platysmal bands. The review of each area encompasses the relevant anatomy, specifics on injection locations and techniques, starting doses (total and per injection point), the influence of other variables, such as gender, and assessment and retreatment issues. Factors unique to each area are presented, and the discussion of each treatment area concludes with a review of key elements that can increase the likelihood of a successful outcome. Summary tables are provided throughout.

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 imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.231

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.086
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0090.005
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0090.005
Research integrity0.0150.013
Insufficient payload (model declined to judge)0.0110.010

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 head0.145
GPT teacher head0.319
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations404
Published2004
Admission routes1
Has abstractyes

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