Real‐World Experience Using a Multi‐Modality System using Intense Pulsed Light, Radiofrequency Microneedling, High‐Intensity Focused Ultrasound, or Thermal Radiofrequency, 808, <scp>HIFU</scp> for Skin Rejuvenation Treatment
Bibliographic record
Abstract
BACKGROUND: Medical aesthetic procedures for facial rejuvenation with laser and energy-based devices (EBDs) are rapidly increasing. The following cases highlight real-life experience using a multi-modality system with various handpieces that combine intense pulsed light (IPL), laser hair removal (808 diode), high-intensity focused ultrasound (HIFU), radiofrequency microneedling (RFM), and thermal radiofrequency (RF) for antiaging and rejuvenation treatment. Laser and RFM treatments may improve skin conditions by inducing cutaneous changes that remodel the skin matrix. METHODS: Six physicians who treat patients for skin rejuvenation reported on clinical cases from their practice using a multi-modality system with various handpieces. RESULTS: During the meeting, the advisors discussed 15 cases and agreed to select seven patients with different ages and skin phototypes receiving various treatments for photodamage of the face, neck, and décolleté. The advisors discussed why they selected the case, previous treatment, type of treatment, results, and clinical pearls. CONCLUSION: Sharing best practices in medical aesthetics using combination treatments on a single multi-modality energy-based device such as laser and MRF for facial, neck, and chest skin may support healthcare providers treating patients for skin rejuvenation to improve clinical outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".