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Record W2891411044 · doi:10.1002/jum.14789

Ultrasonography Technique in Abdominal Subcutaneous Adipose Tissue Measurement: A Systematic Review

2018· review· en· W2891411044 on OpenAlexaff
Alain J. Azzi, Ann‐Sophie Lafrenière, Mirko S. Gilardino, Thomas M. Hemmerling

Bibliographic record

VenueJournal of Ultrasound in Medicine · 2018
Typereview
Languageen
FieldMedicine
TopicBody Contouring and Surgery
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineUltrasonographyAdipose tissueRadiologySubcutaneous adipose tissueUltrasoundReliability (semiconductor)Subcutaneous tissueSubcutaneous fatMedical physicsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

There are currently several reported techniques of sonographic subcutaneous adipose tissue (SAT) measurement described in the literature. This systematic review aims to report techniques of SAT measurement using ultrasonography. A systematic literature search was performed and identified 39 relevant articles using ultrasonography to quantify abdominal SAT. The following parameters were collected: type of measurement, ultrasound machine make/model, transducer frequency, external/internal landmarks, pressure applied on probe, special techniques and inter-/intraobserver reliability. Literature findings related to the above parameters were summarized. A summary of the most common techniques and parameters is provided, serving as a reference for a necessary standardized approach.

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)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.047
GPT teacher head0.341
Teacher spread0.294 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2018
Admission routes1
Has abstractyes

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