Influence of Season on Efficacy and Tolerability of Tazarotene 0.045% Lotion for the Treatment of Acne.
Bibliographic record
Abstract
Objective: analysis evaluated efficacy and safety of tazarotene 0.045% lotion in warmer versus colder months. Methods: In two Phase III, double-blind, 12-week studies, participants aged nine years or older with moderate-to-severe acne were randomized 1:1 to once-daily tazarotene or vehicle lotion. The pooled population (N=1,614) was stratified by randomization date (warmer=May to September; colder=October to April). Evaluations included inflammatory/noninflammatory lesion counts, treatment success, adverse events, and safety/tolerability. Results: <0.001, all). No strong seasonal trends in safety were observed, though tazarotene led to slightly more discontinuations (3.4% vs. 1.9%) and related adverse events (12.0% vs. 10.3%) in colder versus warmer months. Transient increases in scaling, erythema, and itching at Weeks 2 to 8 of tazarotene treatment were slightly higher in colder versus warmer months but returned to baseline/improved by Week 12. Limitations: Geographical variation across study sites can lead to varying temperatures and humidity within the same months. Conclusion: Tazarotene 0.045% lotion was efficacious and well tolerated for acne treatment, regardless of season. Year-round tolerability of tazarotene 0.045% lotion may be due to its lower tazarotene concentration and polymeric emulsion technology, which simultaneously delivers moisturizers/humectants/emollients to skin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".