The role and clinical benefits of high‐intensity focused electromagnetic devices for non‐invasive lipolysis and beyond: A narrative review and position paper
Bibliographic record
Abstract
BACKGROUND: In 2018, the first device to use high-intensity focused electromagnetic (HIFEM) technology to non-invasively build muscle was brought to market. Even more recently, the first HIFEM and radiofrequency combination device designed to both build muscle and eliminate fat cells came into use (HIFEM+). In view of the increase in recently published original data pertaining to HIFEM, an advisory board recently convened to discuss the group's clinical experiences with this technology. AIM: Communicate an advisory group's recommendations for the current use of HIFEM+ technology for aesthetic indications. METHODS: An advisory board meeting to discuss challenges and opportunities for HIFEM devices in aesthetic medicine took place in November 2020, via remote conference. The expert advisory board consisted of a group of senior aesthetic physicians regularly treating patients with non-invasive body contouring devices. A narrative review of the literature and key recommendations from the meeting are presented herein. RESULTS: To date, the combined results of several clinical studies (including over 500 patients and 30 investigators) support that patients treated with HIFEM+ experience on average, 30% less fat, 25% more muscle, 19% reduction in abdominal separation and up to 5.9 cm reduction in waist circumference. Moreover, HIFEM+ induces a 30% increase in satellite cell content, which is similar to the 36% increase observed following twelve weeks of exercise. CONCLUSIONS: The advisory board unanimously agreed on several messages related to HIFEM technology, including that the pairing of HIFEM and radiofrequency (HIFEM+) enables a higher intensity of muscle stimulation and lipolysis, compared to HIFEM alone.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".