Efficacy and tolerability of a double‐conjugated retinoid cream vs 1.0% retinol cream or 0.025% tretinoin cream in subjects with mild to severe photoaging
Bibliographic record
Abstract
BACKGROUND: Topical retinoids are used to treat the visible signs of photoaging. While efficacious, they are irritating. OBJECTIVE: Evaluate the effectiveness and tolerability of a double-conjugate retinoid cream (AlphaRet Overnight Cream; AHA-Ret) in improving visible signs of photoaging vs 1.0% retinol or 0.025% tretinoin. METHODS: A 12-week, split-face, randomized trial was conducted in 48 female subjects, aged 30-65 years with mild to severe photodamage. AHA-Ret was applied to one side of the face and either retinol (n=24) or tretinoin (n=24) to the other side (PM). Expert blinded evaluation of images and Nova measurements occurred at 4, 8, and 12 weeks. Tolerability was assessed throughout the study. RESULTS: Forty-seven subjects completed the study. AHA-Ret demonstrated significant reductions in average severity from baseline: Fine Lines/Wrinkles (P<.001; all time points); Erythema (P=.004, P<.0001; 8 and 12 weeks, respectively); Dyschromia (P<.0001; all time points); Skin Tone (P<.0001; all time points), and Pore Size (P=.035, P<.0001; 8 and 12 weeks, respectively). AHA-Ret induced less Erythema vs retinol at 8 (P=.008) and 12 (P<.02) weeks. AHA-Ret was noninferior to prescription tretinoin in all categories at 4 and 8 weeks, and for Fine Lines/Wrinkles, Erythema, Dyschromia, and Skin Tone at 12 weeks. Improvements in Hydration occurred at every time point with AHA-Ret only (P<.04, P<.03, P<.01). Less irritation was reported with AHA-Ret vs retinol or tretinoin. CONCLUSIONS: Treatment with a double-conjugate retinoid cream demonstrated early reductions in photodamage and improvements in Hydration. AHA-Ret induced less Erythema vs retinol and was more tolerable vs retinol and tretinoin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".