An open‐label clinical trial assessing the efficacy and safety of Bend Skincare Anti‐Aging Formula on minimal erythema dose in skin
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Sunburn and other health risks associated with excess sun exposure place huge economic burdens on societies, and create discomfort and disease within susceptible individuals. Oral supplements that reduce sunburn may be advantageous. This study evaluated the safety and efficacy of Bend Skincare Anti-Aging Formula to ameliorate sunburn induced with a solar simulator. METHODS: Subjects (n = 28) with Fitzpatrick skin phototypes I, II, or III took 4 capsules daily of the supplement providing 1400 mg of eicosapentaenoic acid (EPA) + docosahexaenoic acid (DHA), 120 mg of gamma-linolenic acid (GLA), 5 mg of lutein, 2.5 mg of zeaxanthin, and 1000 IU of vitamin D3 for 8 weeks. Skin on each subject's back was exposed to a progressive sequence of timed ultraviolet (UV) radiation exposure doses at baseline, and after 4- and 8-week treatment to determine their minimal erythema dose (MED). Results were compared before and after treatment using 3 paired t tests and subsequently 3 linear mixed models. RESULTS: Treatment significantly improved tolerance to UV exposure as evidenced by increased MED at 4 and 8 weeks compared with baseline (P < .001). This protection increased with prolonged use of Bend Skincare Anti-Aging Formula as demonstrated by progressively increased MED between baseline and 4 weeks, and again between 4 and 8 weeks (P < .001). Nearly 86% of patients responded to treatment within 4 weeks and 100% of patients responded by the end of the study, resulting in a 39% mean increase in MED at 4 weeks, and an 84% mean increase in MED at 8 weeks compared with baseline. Treatment was well tolerated with no product associated adverse events (AE) and only a few mild and expected side effects. CONCLUSION: Bend Skincare Anti-Aging Formula safely and effectively provides significant skin photoprotection that increases with continued use.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".