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Record W4235119024 · doi:10.1055/b-0041-180098

10 Radiofrequency-Assisted Liposuction for Body Contouring

2021· book-chapter· en· W4235119024 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsLiposuctionBody contouringContouringMedicineComputer scienceSurgeryComputer graphics (images)Internal medicine

Abstract

fetched live from OpenAlex

Body contouring has been addressed through liposuction alone or with skin excision procedures. Traditionally, only individuals with relatively mild skin excess could be managed with liposuction alone. There has long been a need for a technology that can safely and reproducibly tighten skin without lengthy incisions. Radiofrequency (RF) induced thermal contraction has been used in various medical and surgical specialties for many years but was only approved by the FDA in 2016 to assist in body contouring. The radiofrequency-assisted liposuction (RFAL) device Bodytite (Inmode, Ltd, Toronto, Canada) utilizes this effective technology to specifically heat the subcutaneous tissue and the skin in an effective, safe, and reproducible way. In this chapter, we offer an overview of the Bodytite device including the built-in safeguards, a guide to proper patient selection, and highlight the pearls and pitfalls of this exciting technology. RFAL is a powerful tool for thermal contraction of the soft tissues throughout the body in properly selected cases.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.561
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.034
GPT teacher head0.257
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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