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Record W4307973986 · doi:10.1093/asj/sjac267

Integrative Assessment for Optimizing Aesthetic Outcomes When Treating Glabellar Lines With Botulinum Toxin Type A: An Appreciation of the Role of the Frontalis

2022· review· en· W4307973986 on OpenAlexaff
Vince Bertucci, Jean Carruthers, Deborah D Sherman, Conor J. Gallagher, Jessica Brown

Bibliographic record

VenueAesthetic Surgery Journal · 2022
Typereview
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersRevance
KeywordsMedicineBotulinum toxinSurgery

Abstract

fetched live from OpenAlex

Despite the perception that treatment of glabellar lines with botulinum toxin A is straightforward, the reality is that the glabellar region contains a number of interrelated muscles. To avoid adverse outcomes, practitioners need to appreciate how treatment of 1 facial muscle group influences the relative dominance of others. In particular, practitioners need to understand the independent role of the frontalis in eyebrow outcomes and the potential for negative outcomes if the lower frontalis is unintentionally weakened by botulinum toxin A treatment. In addition, practitioners must recognize how inter-individual variation in the depth, shape, and muscle fiber orientation among the upper facial muscles can affect outcomes. For optimal results, treatment of the glabellar complex requires a systematic and individualized approach based on anatomical principles of opposing muscle actions rather than a one-size-fits-all approach. This review provides the anatomical justification for the importance of an integrated assessment of the upper facial muscles and eyebrow position prior to glabellar treatment. In addition, a systematic and broad evaluation system is provided that can be employed by practitioners to more comprehensively assess the glabellar region in order to optimize outcomes and avoid negatively impacting resting brow position and dynamic brow movement.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.043
GPT teacher head0.316
Teacher spread0.273 · 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
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

Citations9
Published2022
Admission routes1
Has abstractyes

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