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Record W4291000611 · doi:10.1093/asj/sjac214

Commentary on: Safety, Pharmacodynamic Response, and Treatment Satisfaction With Onabotulinumtoxin A 40 U, 60 U and 80 U in Subjects With Moderate to Severe Dynamic Glabellar Lines

2022· letter· en· W4291000611 on OpenAlexaff
Jean Carruthers

Bibliographic record

VenueAesthetic Surgery Journal · 2022
Typeletter
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDosingPtosisPharmacodynamicsPatient satisfactionFacial rejuvenationSurgeryInternal medicinePharmacokinetics

Abstract

fetched live from OpenAlex

See the Original Article here. Joseph et al present a well-thought-out Phase 1b dose-ranging, safety, and efficacy study of the effects of escalating doses of onabotulinumtoxin A (ONA) in reduced injection volume in mostly white female subjects with moderate to severe dynamic glabellar rhytides.1 Subject evaluation was garnered with the Facial Line Satisfaction Questionnaire (FLSQ). Cleverly, the authors performed an open-label study in which 80 U of ONA was administered to a subset of 7 women to inform the decision about whether to include this dose in the larger subsequent double-blind study. The study is very well done, and the results compare remarkably exactly with previous high-dose studies. My only question is the lack of any mention of the recovery time for 2 subjects (1 with brow ptosis and 1 with lid ptosis) because there is an unfounded fear that longer duration of effect also correlates with longer-lasting complications. The clinical interest in higher dosing was sparked by the 6-month longevity of daxibotulinumtoxin A (Revance, Nashville, TN) seen in clinical trials.2 Investigators sought to see if increasing dosage in already approved botulinum neurotoxin A (BoNT-A) products would also confer increased longevity and answer the question about natural vs “overdone” results.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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.

Study designCase report
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
Published2022
Admission routes1
Has abstractyes

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