Objective quantification of liposuction results
Bibliographic record
Abstract
Currently, no reliable gold standard exists for the objective outcome measurement following liposuction. The purpose of this systematic review was to summarize reported methods of monitoring liposuction results by objectively measuring subcutaneous adipose tissue. A systematic literature search was performed to identify relevant articles that described techniques for objectively quantifying adipose tissue following traditional liposuction. The search included published articles in three electronic databases-Ovid MEDLINE, Embase, and PubMed. Subcutaneous adipose tissue was estimated using the following techniques: ultrasound, dual-energy X-ray absorptiometry, magnetic resonance imaging, computed tomography, and three-dimensional imaging volumetric analysis. Reported benefits of liposuction objective measurements included providing patients with a quantitative assessment of the liposuction results pre- and postoperatively, detecting significant changes in body fat deposits, and following patterns of fat redistribution. This review provides a summary of various techniques for quantification of liposuction results. More studies are needed to study the clinical relevancy and impact of the various imaging modalities reviewed as well as to develop automated volumetric measurement technology with improved accuracy, efficacy, and reproducibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".