MétaCan
Menu
Back to cohort
Record W2053246699 · doi:10.1097/dss.0000000000000159

Injecting Botulinum Toxin at Different Depths Is Not Effective for the Correction of Eyebrow Asymmetry

2014· article· en· W2053246699 on OpenAlexaff
Jason Sneath, Shannon Humphrey, Alastair Carruthers, Jean Carruthers

Bibliographic record

VenueDermatologic Surgery · 2014
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British ColumbiaSKiN Health
Fundersnot available
KeywordsEyebrowMedicineForeheadGlabellaSurgeryWrinkleBotulinum toxinFrontalis muscle

Abstract

fetched live from OpenAlex

BACKGROUND: It is theorized that brow elevation after treatment with botulinum toxin Type A (BoNT-A) results from inactivation of the brow depressors. Expert consensus is that increased injection depth delivers more BoNT-A to these depressors and causes increased elevation. This technique is applied to the correction of brow height asymmetry. OBJECTIVE: To compare changes in brow height after deep versus shallow BoNT-A in patients with brow asymmetry. METHODS: A prospective split-face analysis was performed on 23 women with eyebrow-height asymmetry. Subjects received 64 units of BoNT-A, divided among 16 injection sites in the glabella, forehead, and lateral canthal area. On the side where increased brow lift was desired, deep injections were performed and shallow injections on the opposite side. Photographs were taken at baseline and Week 4 for comparison measurements. RESULTS: All 23 women enrolled completed baseline injections and returned for the 4-week follow-up. There was no significant difference at 4 weeks in the change in brow height between the sides that received deep versus shallow BoNT-A injection. CONCLUSION: Because of the diffusion of the BoNT-A between muscle layers, the eyebrow depressor muscles cannot be accurately targeted with deep injection into the muscle belly for correction of eyebrow height discrepancies.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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 designObservational
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

Citations10
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueDermatologic SurgerySame topicFacial Rejuvenation and Surgery TechniquesFrench-language works237,207