Expert consensus on perioperative integrated skincare for noninvasive energy‐based device aesthetic procedures in clinical practice in China
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'.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".