Off-Label Uses of Topical Vitamin D in Dermatology: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Topical vitamin D is approved by the US Food and Drug Administration for the treatment of psoriasis but is also used off-label in the treatment of a variety of cutaneous diseases despite a lack of evidence-based guidelines. OBJECTIVE: The objective of this study was to provide evidence-based clinical guidelines for the off-label use of topical vitamin D in the treatment of dermatologic disease. METHODS: A systematic literature review was conducted via the MEDLINE, Embase, and CENTRAL databases for off-label uses of topical vitamin D analogues in the treatment of dermatologic disease other than psoriasis. The data were synthesized, and evidence-based recommendations were rendered according to the highest level of evidence available. RESULTS: A total of 165 articles met the inclusion criteria. A moderate to strong recommendation was given for the use of topical vitamin D in combination with corticosteroids and phototherapy in vitiligo and as monotherapy for various ichthyoses, morphea, pityriasis alba, prurigo nodularis, and polymorphous light eruption. There is evidence showing that topical vitamin D is ineffective in the treatment of actinic keratosis, seborrheic keratosis, lichen planus, seborrheic dermatitis, alopecia areata, chemotherapy-induced alopecia, and hypertrophic scars. CONCLUSION: Topical vitamin D analogues have an important role in the off-label treatment of dermatologic disease, but higher quality studies are still required.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".