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Record W2174409391 · doi:10.1093/asj/sjv044

Encyclopedia of Aesthetic Rejuvenation Through Volume Enhancement

2015· article· en· W2174409391 on OpenAlexaboutno aff
Wayne Carman

Bibliographic record

VenueAesthetic Surgery Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsEncyclopediaMedicineFacial rejuvenationSection (typography)Theme (computing)Art historyLibrary scienceSurgeryComputer scienceArtWorld Wide Web

Abstract

fetched live from OpenAlex

Charles K. Herman and Berish Strauch, eds. Encyclopedia of Aesthetic Rejuvenation Through Volume Enhancement . New York, NY: Thieme, 2014. ISBN-10: 1604067039, ISBN-13: 978-1604067033, $259.99. ![Graphic][1] Webster defines an encyclopedia as, “a work that contains information on all branches of knowledge or treats comprehensively a particular branch of knowledge usually in articles arranged alphabetically often by subject”.1 This book by Charles K Herman and Berish Strauch qualifies as being encyclopedic in that it presents a comprehensive summary of surgical volume enhancement. The book is arranged into five sections: Basic Principles, Face, Breast, Extremities, and Buttocks and Trunk. It contains a total of 409 pages with 49 chapters from internationally respected authors. The editors introduce the concept of volume rejuvenation in Chapter 1 and establish their premise for this book. Chapter 2 is a historical review of the various synthetic and biological materials used by surgeons to alter volumes. The Face section contains a total of 19 chapters describing a broad range of techniques with the common theme of volume enhancement. Topics include dermal fillers, fat injection, facial implants, and augmentation rhinoplasty. The Breast section, containing 13 chapters, includes aesthetic implant surgery, fat injection techniques and indications, breast reshaping after massive weight loss, … Corresponding Author: Dr Wayne Carman, 325 Eglinton Avenue East, Toronto, ON, M4P 1L7, Canada. E-mail: wwcmd{at}aol.com [1]: /embed/inline-graphic-1.gif

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.650

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.044
GPT teacher head0.295
Teacher spread0.251 · 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 designNot applicable
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

Citations0
Published2015
Admission routes1
Has abstractyes

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