Xeroderma pigmentosum: an updated review
Bibliographic record
Abstract
Background: Early recognition of xeroderma pigmentosum is important to minimize the complications arising from the harmful effects of exposure to ultraviolet radiation. This narrative review aims to familiarize physicians with the clinical features, diagnosis and management of xeroderma pigmentosum. Methods: A search was conducted in December 2021 in PubMed Clinical Queries using the key term "xeroderma pigmentosum". The search strategy included all clinical trials, observational studies and reviews published within the past 10 years. The information retrieved from the search was used in the compilation of this article. Results: laser resurfacing, fractional/pulsed laser therapy, and photodynamic therapy. Cutaneous malignancy can be treated by photodynamic therapy, curettage and electrodesiccation, or surgical excision. Oral isotretinoin, oral niacinamide, topical imiquimod and topical fluorouracil can be used for the prevention of skin malignancy. Treatment options for poikiloderma include chemical peeling, dermabrasion and laser resurfacing. Methylcellulose eyedrops and soft ultraviolet-protective contact lenses may be used to keep the cornea moist and protect against the harmful effects of keratitis sicca. Investigational therapies include the use of T4 endonuclease-V liposome lotion and oral nicotinamide to reduce the rate of actinic keratoses and non-melanoma skin cancers and gene therapy for radical cure of this condition. Conclusion: Although currently there is no cure for xeroderma pigmentosum, increased awareness and early diagnosis of the condition, followed by rigorous sun avoidance and protection and optimal management, can dramatically improve the quality of life and life expectancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".