Moisturizers and Ceramide-containing Moisturizers May Offer Concomitant Therapy with Benefits.
Bibliographic record
Abstract
INTRODUCTION: Ceramide-containing moisturizers may offer benefits as an adjunct to acne treatment. METHODS: An expert panel of 11 Canadian dermatologists, including an international clinical scientist, used the following modified Delphi process: 1) A systematic literature review for acne treatment, dry skin, irritation, depletion of ceramides in acne, and benefits of moisturizers and ceramide-containing moisturizers was conducted; 2) panel members gave their opinion on the resulting statements, taking into account their treatment practices; 3) a panel meeting was held during the 2011 Canadian Dermatology Update to determine final statements; 4) the panel reviewed the final document. RESULTS: The panel reached the following consensus (11/11): 1) A very important reason for nonadherence to acne treatment is dry skin and irritation; 2) skin barrier dysfunction may contribute to acne; 3) dry skin and irritation commonly results from topical acne treatment; 4) dry skin and irritation commonly results from systemic retinoid therapy; 5) moisturizers can improve dryness and irritation resulting from acne treatment; 6) ceramide-containing moisturizers may enhance adherence and complement existing acne therapies; 7) adjunctive therapy with moisturizers should be considered in acne-treated patients. CONCLUSION: The panel proposes that adjunctive therapy with moisturizers, particularly ceramide-containing moisturizers, should be considered in acne-treated patients.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".