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Record W3035680228 · doi:10.1093/asj/sjaa068

A Simple Technique to Measure the Volume of Removed Buccal Fat

2020· article· en· W3035680228 on OpenAlexaff
Chew Lip Ng, Richard Rival, Philip Solomon

Bibliographic record

VenueAesthetic Surgery Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineMeasure (data warehouse)Simple (philosophy)Buccal administrationVolume (thermodynamics)DentistryData mining

Abstract

fetched live from OpenAlex

Buccal fat removal is a cosmetic procedure performed to reduce cheek “chubbiness” and create a more sculpted appearance of the face (Figure 1). It is commonly performed transorally via an incision in the buccal mucosa, followed by dissection into the buccal cheek fat and excising an appropriate amount of fat (Figure 2),1,2 or transposing the fat to volumize other craniofacial regions.3 One ultrasonographic study estimated the volume of buccal fat per cheek to be 11.67 mL and reported that the average amount of fat removed was 2.74 mL.4 Although the procedure is relatively simple, a challenge surgeons face is estimating the amount of fat removed and ensuring that equal amounts of fat are removed from both cheeks. Failure to do so may lead to facial asymmetry. To solve this issue, we describe a simple technique utilizing a syringe and needle to estimate the volume of fat removed intraoperatively. This method also allows for documentation of the volume of fat excised in the operative notes, which is necessary from a medicolegal point of view particularly in cosmetic surgery, and can help with pre- and postoperative counselling of patients. (A, C) Preoperative and (B, D) 1-year postoperative photographs of a 40-year-old woman who underwent bilateral buccal fat excision. Submental liposuction was also performed in this patient. Intraoperative photograph of left-sided buccal fat excision in a 21-year-old woman. The plunger of a 5-mL Luer lock syringe is removed and a 25-gauge fine needle is screwed onto the Luer lock end. Excised fat from one cheek is inserted into the syringe through the plunger end. The plunger is reinserted, taking care to minimize air trapping between the fat and the plunger. The fat is pushed down the syringe to the needle end by the plunger. The needle allows air expulsion but prevents fat extrusion as the fat occludes the fine lumen of the needle. The volume of fat is determined by reading the measurements on the syringe, in milliliters (Figure 3). This is repeated for the fat excised from the contralateral side and the volumes are compared. If the volumes of fat excised are not identical, or inadequate fat is excised, more fat can be excised until equal, or adequate, volumes of fat are excised. A video of the technique is available online as Supplementary Material at www.aestheticsurgeryjournal.com. Excised buccal fat in a 5-mL syringe. The volume of fat is measured by reading off the graduations on the syringe. Watch now at http://academic.oup.com/asj/article-lookup/doi/10.1093/asj/sjaa068 We have utilized this technique between January 2017 and October 2019 in a total of 92 procedures of bilateral buccal fat removal in 76 females and 16 males (age range, 18-44 years; mean age, 25.4 years). The average amount of fat removed per cheek is 2.6 mL (range, 1.9-3.6 ml). This is in line with what other surgeons have reported.1 We have found that this amount of excised fat is sufficient to create a clinically apparent reduction in cheek “chubbiness.” In conclusion, we have found this method of volumetric measurement of fat to be very useful in our practice and we believe it will be helpful to other surgeons as well. The authors declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The authors received no financial support for the research, authorship, and publication of this article.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.043
GPT teacher head0.278
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations5
Published2020
Admission routes1
Has abstractno

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