Expert consensus on perioperative integrated skincare for noninvasive energy‐based device aesthetic procedures in clinical practice in China
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: Noninvasive energy-based device (NI-EBD) aesthetic procedures has recently gained widespread usage for treating various skin conditions, enhancing skin texture and performing rejuvenation-related procedures. However, practically all NI-EBD procedures result in variable degrees of damage to the skin barrier, inducing pathological and physiological processes such as oxidative stress and inflammation, and only a small percentage of individuals possess the innate ability to restore it. OBJECTIVE: To introduce the concept of integrated skincare and establish standardized operational procedures for perioperative integrated skincare, and furnish a theoretical basis for clinical diagnosis and treatment performed by professional medical aestheticians. METHODS: The author leveraged domestic and international guidelines, clinical practice expertise and evidence-based research, adapting them to suit the specific circumstances in China. RESULTS: The consensus were provided four parts, including concept and essence of integrated skincare, integrated skincare significance during the perioperative phase of NI-EBD procedures, active ingredients and functions of effective skincare products, standardized perioperative skincare procedure for NI-EBD procedures and precautions. For the standardized perioperative skincare procedure, four recommendations were listed according to different stages during NI-EBD procedures. CONCLUSION: These recommendations create the 'Expert Consensus on Perioperative Integrated Skincare for Noninvasive Energy-Based Device Aesthetic Procedures in Clinical Practice in China'.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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