The role and clinical benefits of high‐intensity focused electromagnetic devices for non‐invasive lipolysis and beyond: A narrative review and position paper
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: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 | 0.001 |
| 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