Efficacy of Endolift laser for arm and under abdomen fat reduction
Bibliographic record
Abstract
BACKGROUND: Noninvasive laser for body fat contouring is a quickly growing field in the cosmetic dermatology. Surgical options carry disadvantages, such as the usage of anesthetics, swelling, pain and long time for recovery, so there is a growing public request for the techniques with fewer side effects and shorter recovery periods. Several new noninvasive body contouring ways have been advanced such as, cryolipolysis radiofrequency energy, suction-massage, high-frequency focused ultrasound, and laser therapy. Noninvasive laser improves the body's appearance by the elimination of excess adipose tissue, specifically in areas in which fat perseveres in spite of diet and exercise. METHODS: In this study the efficacy of Endolift laser was evaluated for reduction of excess fat in the arms and under abdomen. Ten patients with excess fat in the arms and under abdomen were enrolled in this study. The patients were treated by Endolift laser in the arms and under abdomen areas. The outcomes were evaluated by two blinded board certified dermatologists and by patients' satisfaction. The circumference of each arm and under abdomen was measured using a flexible tape measure. RESULTS: The results showed reduction in the fat and circumference of arms and under abdomen after treatment. The treatment was considered as effective methods with high patient satisfaction. Also no severe adverse effects were reported. CONCLUSION: Endolift laser can be a good alternative to surgical body fat contouring due to its efficacy, safety, minimal recovery time, low cost. Also Endolift laser does not require general anesthetics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".