The Efficacy of Topical Vitamin C and Microneedling for Photoaging
Bibliographic record
Abstract
Background: Photoaging is premature skin aging caused by exposure to ultraviolet (UV) radiation. Vitamin C is an antioxidant that inhibits the tyrosinase enzyme that can reduce pigmentation. Microneedling procedure can improve the penetration of topical vitamin C, and it has skin rejuvenating effects to reduce wrinkles and minimize pore size. Purpose: The main purpose of this study was to evaluate the efficacy of topical vitamin C application after microneedling intervention for the clinical improvement of photoaging. Methods: Twenty-four women with photoaged skin participated in this randomization study, and they were divided into control and intervention groups. Solution of 0.9% NaCl and microneedling were performed to control group, and topical vitamin C and microneedling were performed to intervention group. Three intervention sessions were repeated at a 2 week interval. Signs of photoaging such as pigmentation, wrinkles, and pores were evaluated using Metis DBQ3-1, and the data were obtained numerically. Result: The data analysis revealed a significant improvement in pigmentation in the intervention group compared to control group (p<0.05). Wrinkles and pores evaluation revealed no significant difference between the control and intervention groups. Conclusion: Topical vitamin C after microneedling procedure has provided a significant improvement in pigmentation compared to NaCl 0.9% after microneedling.
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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".