Effect of Endolift laser on upper eyelid and eyebrow ptosis treatment
Bibliographic record
Abstract
Drooping of the upper eyelid and eyebrow (ptosis) is common among people and cause the patients dissatisfaction. Various methods have been developed to treatment of the upper eyelid and eyebrow ptosis. However, the current methods focus on surgery to improve the disorder. But patients are worried about the risks of the procedure, and seeking for a non-invasive alternative method. Therefore, non-invasive methods with consistent efficient improvement are needed, especially for middle-aged patients. This study was conducted of 9 patients who underwent the upper eyelid and eyebrow ptosis. Endolift laser method was used to treat the patients' upper eyelid and eyebrow ptosis. The biometric assessment was used to evaluate the efficiency of the technique. Also the results were evaluated by 3 board-certified dermatologists (blind). Additionally, patients' satisfaction was evaluated at the end of the treatment. The biometric results showed that Endolift laser can increase the thickness, density, and elasticity of the skin in the eyelid area. The patient's satisfaction results showed excellent improvement in the 90% of patients. The results by the dermatologist displayed improvement in about 90% of patient. Endolift laser has been proved efficient and consistent for upper eyelid and eyebrow ptosis rejuvenation and 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".