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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it