Beyond Ultraviolet: A Scoping Review of Light Treatments in Atopic Dermatitis
Bibliographic record
Abstract
Phototherapy has been long recognized as a useful tool in dermatology, and while UV therapy has proven to be successful in many conditions, other forms of light therapy also have positive evidence of efficacy for skin conditions, including atopic dermatitis (AD). The purpose of this review is to examine the existing literature using light as a treatment for AD in humans, outside of conventional UV phototherapy. Literature search of databases and search engines Embase (Elsevier), Web of Science (Clarivate), Pubmed (NLM/NIH), CINAHL (EBSCO), and Google Scholar was performed. Fifteen studies were found: 8 investigated heliotherapy and climatotherapy, 4 examined blue light therapy, and the remaining 3 examined other forms of light therapy such as low-level laser therapy, intense pulsed light therapy, and pulsed-dye laser treatment. The studies found were heterogeneous and this heterogeneity severely limits more nuanced meta-analysis or more definitive conclusions. Light therapy appears to be extremely promising in treating AD and despite its long history, continues to evolve. Non-UV forms of light are particularly notable for the treatment of AD due to their lower risk profile. This article highlights the potential of these therapies and the need for continued research to obtain a homogenous body of literature that can inform clinicians in their practice.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".