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Record W1995309522 · doi:10.1177/1090820x12474628

Expanding Treatment Options for Neuromodulators

2013· article· en· W1995309522 on OpenAlexfundno aff
Foad Nahai, Zbigniew Lorenc, Jeffrey M. Kenkel, Steven Fagien, Haideh Hirmand, Mark S. Nestor, Anthony P. Sclafani, Jonathan M. Sykes, Heidi A. Waldorf

Bibliographic record

VenueAesthetic Surgery Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
FundersAllerganGaldermaValeant Pharmaceuticals InternationalSanofi
KeywordsMedicineIntensive care medicineMEDLINE

Abstract

fetched live from OpenAlex

In the early 21st century, members of the medical community have been both witnesses to and participants in significant changes in the field of aesthetic surgery and medicine, including nonsurgical facial rejuvenation. Although some of dermal fillers were available prior to 2000, their indications were primarily therapeutic. Now, various categories of dermal fillers exist, including hyaluronic acid, poly-L-lactic acid, polymethylmethacrylate, calcium hydroxylapatite, and patients' own fibroblasts. Neuromodulators (ie, botulinum toxin type A [BoNTA] formulations) were first approved for therapeutic purposes but quickly gained popularity for aesthetic applications. According to the American Society for Aesthetic Plastic Surgery, in 2010, more than 2.4 million patients received treatment with a BoNTA product. Clearly, BoNTA injections have been widely adopted in aesthetic practice, beginning with the approval of onabotulinumtoxinA (Botox; Allergan, Irvine, California) in 2002 by the US Food and Drug Administration (FDA) for the temporary improvement in the appearance of moderate to severe glabellar lines associated with corrugator and/or procerus muscle activity in adults 65 years or younger. Off-label applications in areas such as horizontal forehead lines, “crow's feet,” and the perioral area followed.

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 score0.934
Threshold uncertainty score0.488

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 integrity0.0000.000
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 head0.044
GPT teacher head0.284
Teacher spread0.240 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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