Real-World Experience to Understand the Usage of Sebamed® Antibacterial Cleansing Foam in the Prevention of Acne
Bibliographic record
Abstract
Background: Acne vulgaris is a common condition affecting millions globally, with a high prevalence during puberty. Adult-onset acne, particularly in women, is on the rise. Treatment focuses on sebum control and improving appearance, but adherence poses challenges. Aim and objective: This study aimed to assess the effectiveness of Sebamed® antibacterial cleansing foam in preventing acne in real-world settings in India. Methods: This study was a retrospective, multi-centric observational study conducted in 51 Indian healthcare centres. It focused on adult patients with acne vulgaris treated with Sebamed® antibacterial cleansing foam. The study included adult patients of either sex with mild-to-moderate acne or a propensity to develop acne without known allergies or sensitivities to cosmetic products. Results: In this retrospective analysis of 486 patients, the majority had moderate to severe acne (Grade 2 and Grade 3). Most patients had oily skin (47.9%) and facial acne (92.5%). Sebamed® patients with different grades of acne preferred antibacterial cleansing foam and the median duration of treatment was 2 months. Most patients used the foam twice a day and substantial improvement was observed in 56.4%, with a complete resolution of acne in 3.5%. Most patients experienced better to much better efficacy outcomes in different acne parameters, including oiliness (92.2%), papules (90.7%), pustules (86.3%), nodules (82.1%), open comedones (81.9%) and closed comedones (77.2%). Side effects were observed in 2.5% of patients. Conclusion: Sebamed® antibacterial cleansing foam shows promising effectiveness and tolerability as a treatment for acne vulgaris. Further research is needed to validate these findings in larger populations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".