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Record W1842705983 · doi:10.1093/asj/sjv190

Characterizing the Lateral Border of the Frontalis for Safe and Effective Injection of Botulinum Toxin

2015· article· en· W1842705983 on OpenAlexfundno aff
You‐Jin Choi, Sung-Yoon Won, Jae-Gi Lee, Kyung‐Seok Hu, Sung-Taek Kim, Tanvaa Tansatit, Hee‐Jin Kim

Bibliographic record

VenueAesthetic Surgery Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
FundersYork University
KeywordsMedicineBotulinum toxinSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The forehead is a common site for injection of botulinum neurotoxin type A (BoNT-A) to treat hyperactive facial muscles. Unexpected side effects of BoNT-A injection may occur because the anatomy of the forehead musculature is not fully characterized. OBJECTIVES: The authors described the lateral border of the frontalis in terms of facial landmarks and reference lines to determine the safest and most effective forehead injection sites for BoNT-A. METHODS: The hemifaces of 49 embalmed adult Korean cadavers were dissected in a morphometric analysis of the frontalis. L2 was defined in terms of FT (the most protruding point of the frontotemporal region), L0 (the line connecting the infraorbital margin with the tragus), and L1 (the line parallel to L0 and passing through FT) such that L2 was positioned 45° from L1 and passed through FT. RESULTS: The distance from FT to the superior margin of the orbicularis oculi was 12.3 ± 3.3 mm. The frontalis extended more than 5 cm along L2 in 49 of 49 cases (100%), more than 6 cm in 47 cases (95.9%), more than 7 cm in 34 cases (69.4%), more than 8 cm in 11 cases (22.4%), and more than 9 cm in 3 cases (6.1%). The lateral border of the frontalis ran parallel to and within 1 cm of the medial side of L2. CONCLUSIONS: Surface anatomy mapping can assist with predicting the lateral border of the frontalis to minimize the side effects and maximize the efficiency of BoNT-A injections into the forehead.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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