Ultrasound Calculation of Fat Volume for Liposuction: A Clinical Software Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Fat manipulation procedures such as liposuction contain a degree of subjectivity primarily guided by the surgeon's visual or tactile perception of the underlying fat. Currently, there is no cost-effective, direct method to objectively measure fat depth and volume in real time. OBJECTIVES: Utilizing innovative ultrasound-based software, the authors aimed to validate fat tissue volume and distribution measurements in the preoperative setting. METHODS: Eighteen participants were recruited to evaluate the accuracy of the new software. Recruited participants underwent ultrasound scans within the preoperative markings of the study area before surgery. Ultrasound-estimated fat profiles were generated with the in-house software and compared directly with the intraoperative aspirated fat recorded after gravity separation. RESULTS: Participants' mean age and BMI were 47.6 (11.3) years and 25.6 (2.3) kg/m2, respectively. Evaluation of trial data showed promising results following the use of a Bland Altman agreement analysis. For the 18 patients and 44 volumes estimated, 43 of 44 measurements fell within a confidence interval of 95% when compared with the clinical lipoaspirate (dry) volumes collected postsurgery. The bias was estimated at 9.15 mL with a standard deviation of 17.08 mL and 95% confidence interval between -24.34 mL and 42.63 mL. CONCLUSIONS: Preoperative fat assessment measurements agreed significantly with intraoperative lipoaspirate volumes. The pilot study demonstrates, for the first time, a novel companion tool with the prospect of supporting surgeons in surgical planning, measuring, and executing the transfer of adipose tissues.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it