Endolift laser an effective treatment modality for forehead wrinkles and frown line
Bibliographic record
Abstract
BACKGROUND: The search of beauty and youth has received a lot of attention which is proved by increasing cosmetic techniques. The people prefer non-surgical and invasive method for reduction and wrinkles treatment. METHODS: In this study, we used Endolift laser for forehead wrinkles and frown line treatment to evaluate the clinical safety and effectiveness of this technique for reduction of forehead wrinkles and frown line. A total of 9 patients with forehead wrinkles and frown line were included in the current study. The results were investigated using biometric evaluation. Also, assessment was performed clinically and photographically, and physician's assessment and patient satisfaction responses were recorded. RESULTS: According to the biometric results, the skin thickness and elasticity significantly increase after Endolift laser treatment. According to the physician's assessment, 90% of patients displayed very much improvement after Endolift laser treatment, and according to the patient assessment, 91% of patients reported positive satisfaction response. CONCLUSION: Treatment with Endolift laser is safe and an effective method for decrease of forehead wrinkles and frown line treatment. It offers as a non-invasive alternative technique in compared to other invasive procedures for forehead wrinkles and frown line treatment.
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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 | 0.000 |
| 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".