Tazarotene versus tazarotene plus hydroquinone in the treatment of photodamaged facial skin: A multicenter, double‐blind, randomized study
Bibliographic record
Abstract
Objectives: To compare the efficacy and tolerability of tazarotene plus hydroquinone versus tazarotene alone in the treatment of facial photodamage. Methods: Patients with facial mottled hyperpigmentation of at least moderate severity and an overall integrated assessment of photodamage score of at least moderate applied tazarotene 0.1% cream each evening and either hydroquinone 4% cream or placebo cream each morning for up to 24 weeks. Results: Among 131 patients enrolled, 114/124 (92%) with exit data completed. Both regimens were highly effective in reducing photodamage, with tazarotene plus hydroquinone showing superiority over tazarotene alone for some efficacy measures. The incidence of ⩾1‐grade improvement from baseline (on a scale of none, minimal, mild, moderate, or severe) was significantly greater with tazarotene plus hydroquinone than with tazarotene alone for lentigines (weeks 12–24, p⩽0.01) and mottled hyperpigmentation (week 16, p⩽0.05). The incidence of ⩾50% global improvement was also significantly superior with the combination regimen as early as week 8 (p⩽0.01). Both regimens were associated with good tolerability and high patient satisfaction (no significant between‐group differences). Conclusions: The adjunctive use of hydroquinone can enhance the efficacy of tazarotene in reducing dyspigmentation associated with photodamage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".