A Randomized, Single-Blind, Postmarketing Study of Multiple Energy Levels of High-Intensity Focused Ultrasound for Noninvasive Body Sculpting
Bibliographic record
Abstract
BACKGROUND: High-intensity focused ultrasound (HIFU) is a nonsurgical, noninvasive body sculpting method. OBJECTIVE: To investigate preferences for treatment settings using a HIFU device. MATERIALS AND METHODS: HIFU was applied to the anterior abdomen in three passes of decreasing depth (1.6, 1.3, and 1.1 cm) in patients randomized to HIFU energy levels (each of 3 passes [total]) of 47 (141), 52 (156), or 59 (177) J/cm(2). The primary assessment was week 12 post-treatment change from baseline waist circumference at the level of the iliac crest for all treatment groups combined. RESULTS: The primary assessment achieved statistical significance (least squares mean 2.51 cm, 95% confidence interval [CI] = -3.14 to -1.88; p < .001), with no significant differences between groups. At week 12, 69% to 86% of patients and 73% to 79% of investigators rated appearance as improved or much improved. The average worst pain (100-mm visual analog scale) experienced during treatment was mild (47 J/cm(2): 17.1 mm, 95% CI = 4.33-29.81 mm; 52 J/cm(2): 24.6 mm, 95% CI = 12.24-36.95 mm; 59 J/cm(2): 30.9 mm, 95% CI = 18.71-43.17 mm). There were no serious adverse events. CONCLUSION: HIFU treatment at different energy levels and multiple tissue depths was well tolerated and effective in reducing waist circumference.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".