Improvement in Atopic Dermatitis Using a Novel Topical 2% Cannabidiol (CBD) Application
Bibliographic record
Abstract
ABSTRACT Background Atopic dermatitis (AD) is a prevalent chronic inflammatory skin condition characterized by Th2‐type inflammation, significantly impacting patients' quality of life. Patients often experience dryness, severe itching, and skin lesions, necessitating interventions to manage discomfort and flare‐ups. Common treatments, such as topical corticosteroids and calcineurin inhibitors, are effective but have adverse effects, making them unsuitable for long‐term use. Consequently, there is an ongoing effort to find sustainable and safe alternative treatments. Recently, cannabidiol (CBD), a non‐psychoactive compound derived from the Cannabis sativa plant, has gained attention in dermatology for its anti‐inflammatory properties. Methods This pilot study utilized a 4‐week open‐label observational prospective cohort design to evaluate the effects of a 2% CBD cream‐based topical application in patients ( n = 19) with moderate‐to‐severe atopic dermatitis. Participants were given the topical CBD product to apply daily to affected areas and were examined at baseline, week 1, week 2, week 4, and week 8. Primary outcomes measured included the appearance of eczema lesions and patient satisfaction with the treatment. Results Participants reported improvements in skin hydration, comfort, inflammation relief, and overall skin appearance. Various objective evaluations and clinical photography were consistent with patient reported improvement. Conclusions These results support the potential of CBD as a sustainable and effective treatment option for mild to moderate atopic dermatitis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".