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Record W2383480060 · doi:10.1097/dss.0000000000000510

A Pilot Study on the Treatment of Posterior Cheek Enlargement in HIV+ Patients With Botulinum Toxin A

2015· article· en· W2383480060 on OpenAlexaff
Christina Scali, Alastair Carruthers, Dean Malpas, Shannon Humphrey

Bibliographic record

VenueDermatologic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCheekMedicineBotulinum toxinMasseter muscleTolerabilitySurgeryParotid glandAnatomyDentistryInternal medicineAdverse effect

Abstract

fetched live from OpenAlex

BACKGROUND: Posterior cheek enlargement in human immunodeficiency virus (HIV)+ individuals can lead to significant cosmetic disfigurement. Both parotid gland and masseter muscle overlie the mandibular ramus, contributing to lower facial contour. However, posterior cheek enlargement has not been well characterized anatomically. There are also limited treatment options. Botulinum toxin is a highly efficacious minimally invasive option for improving the shape of the lower face. OBJECTIVE: A pilot study was undertaken to better characterize posterior cheek enlargement in HIV+ patients and explore treatment with botulinum toxin A. MATERIALS AND METHODS: Five HIV+ patients with posterior cheek enlargement were treated with botulinum toxin A. Clinical, photographic, and radiological evaluations allowed the precise calculation of any change in volumes resulting from botulinum toxin A. RESULTS: All 5 patients had a good response with a mean decrease of 21.4% and 11.2% in the volumes of the masseter muscle and parotid gland, respectively. The effect was long lasting even at 6 months after injection and well tolerated. CONCLUSION: Botulinum toxin A may be a less invasive treatment of posterior cheek enlargement in HIV+ patients, with advantages of a good result that is long lasting with good tolerability and minimal risk.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.347

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 head0.085
GPT teacher head0.291
Teacher spread0.205 · 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
Published2015
Admission routes1
Has abstractyes

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