Effects of Topical Copper Tripeptide Complex on CO <sub>2</sub> Laser–Resurfaced Skin
Bibliographic record
Abstract
OBJECTIVE: To evaluate the role of skin care products that contain a copper tripeptide complex, glycyl-l-histidyl-l-lysine-Cu(2+) (GHK-Cu), in treating carbon dioxide (CO(2)) laser-resurfaced skin. METHODS: Patients meeting the inclusion criteria underwent circumoral skin resurfacing using a CO(2) laser at standard settings. Patients were then randomized to receive posttreatment skin regimens with or without GHK-Cu. Evaluations for erythema throughout the posttreatment period were performed using computer software and blinded evaluators. In addition, overall improvement in wrinkles and overall improvement in skin appearance 12 weeks after treatment were assessed. Patients completed a validated questionnaire before and 12 weeks after treatment. RESULTS: Thirteen patients completed the study. Computer analysis and blinded evaluators found no statistically significant differences between groups for earlier resolution of erythema. All the patients experienced significant improvement in wrinkles and overall skin quality, but no differences were found between groups. The results of the questionnaire indicated a significant difference in the posttreatment improvement of overall skin quality for patients using GHK-Cu (P = .04). CONCLUSIONS: Copper tripeptide complex (GHK-Cu) skin care products placed on CO(2) laser-resurfaced skin offered no significant reduction or resolution of posttreatment erythema. Objective evaluation found no significant improvement in wrinkles or overall skin quality. However, patient satisfaction was significantly higher for those who used GHK-Cu skin care products after CO(2) laser skin resurfacing.
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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) | 0.001 | 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".