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Record W2313857928 · doi:10.3109/14764172.2016.1157374

Quantification of adipose volume reduction with a prospective study analyzing the application of external radiofrequency energy and high voltage ultrashort pulse duration electrical fields

2016· article· en· W2313857928 on OpenAlexaff
Diane Duncan, Theresa Kim, Robbin Temaat

Bibliographic record

VenueJournal of Cosmetic and Laser Therapy · 2016
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsYork University
Fundersnot available
KeywordsMedicineUltrasoundNuclear medicineCircumferenceSurgeryBiomedical engineeringRadiologyMathematics

Abstract

fetched live from OpenAlex

To date, there have been no objective measurements of subcutaneous volume loss following treatments with a noninvasive radiofrequency (RF)-based device. Twenty female patients were treated with a suction-coupled bipolar RF device using external RF energy combined with pulsed electromagnetic RF energy for subcutaneous fat reduction. Parameters followed included weight, Vectra measurements of abdominal circumference and torso volume, and high-definition ultrasound measurements of fat thickness. Measurements were taken before treatment and three times following treatment. Analysis of the measured parameters showed that mean circumference reduction of 2.30 cm was noted at three months post-treatment. Independent volumetric analysis showed a mean subcutaneous volume reduction of 428 cc three months following RF treatment. High-resolution ultrasound fat thickness was reduced by a mean of 39.6% three months following the final BodyFX treatment. Independent and paired-sample t-tests showed a p value of < 0.05. Repeated measures of analysis of covariance (ANCOVA) adjusted for differences in age, as well as height and weight (proxy for body mass index) to minimize individual differences and control for extraneous variables that may affect the pre- and post-treatment results were analyzed. No confounding variables were found. All analyses were conducted using IBM SPSS 21.0.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.155

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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