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Record W4293425816 · doi:10.3390/jcm11175038

The Jaws Brachioplasty: An Original Technique: Improving Aesthetic Outcomes in Arm Lift Procedures

2022· article· en· W4293425816 on OpenAlexaboutno aff
Giuseppe Nisi, Francesco Ruben Giardino, Martino Giudice, Giorgio Fasano, Roberto Cuomo, Luca Grimaldi

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsnot available
FundersUniversità degli Studi di Siena
KeywordsMedicineLift (data mining)OrthodonticsData mining

Abstract

fetched live from OpenAlex

(1) Background: The increase in the number of bariatric surgery procedures has led plastic surgeons to look for new approaches to improve outcomes of body-contouring surgeries. A major concern in brachioplasty is the scarring process. Here, we propose a novel technique to minimize the incidence of pathological or unsatisfactory scars from brachioplasty. A video of the entire procedure is provided. (2) Methods: From January 2016 to August 2020, we performed the "Jaws" brachioplasty on 16 post-bariatric patients. We evaluated the effectiveness of the technique through pre- and postoperative assessments by patients and surgeons, the Vancouver Scar Scale, and the detection of major and minor complications within 12 months of follow-up. (3) Results: Thirteen patients were female and three were male, with a mean age of 32.5 ± 6.8 years (range: 22-47 years). The BODY-Q© Arms Section scores improved significantly, with no incidence of major or minor complications over 1 year of follow-up, and favorable aesthetic outcomes. (4) Conclusions: We believe that the "Jaws" technique is a valid contribution to post-bariatric surgery, as it aims to solve specific aesthetic problems of scarring from brachioplasty. The small number of patients does not allow the comparison of our original technique to others previously described in the literature.

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)0.010
metaresearch head score (Gemma)0.007
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 score0.260
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.007
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 integrity0.0000.002
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 head0.402
Teacher spread0.358 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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