Treatment of horizontal neck wrinkles by Endolift laser: Biometric measurement
Bibliographic record
Abstract
BACKGROUND: One of the common esthetic complaints of patients is horizontal neck wrinkles that have limited treatment modality. AIM: In the present study, we evaluated the efficacy of the Endolift laser on the horizontal neck wrinkles. METHODS: Totally, 10 healthy female and male patients suffering horizontal neck wrinkles were joined in this study. All patients are treated with Endolift laser. The effect of the Endolift methods on the horizontal neck wrinkles was evaluated by biometric parameter changed results with Cutometer, Visioface, and the Skin Ultrasound Imaging system. Also, three blinded dermatologists and patients' satisfaction were evaluated. RESULTS: The Visoface results showed that the Endolift laser treatment significantly decline the depth and area of horizontal neck wrinkle. The skin ultrasonography results reported the epidermis and dermis density and thickness were significantly increased. Also, the cutometer outcomes displayed that the Endolift laser treatment can increase skin elasticity. Also, significantly a greater number of patients were well satisfied with the technique. CONCLUSION: In conclusion, Endolift laser is a safe and effective method for decreasing the horizontal neck wrinkles and improving the appearance of the neck. This procedure does not require general anesthesia and recovery time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".