Nail Psoriasis: Diagnosis, Assessment, Treatment Options, and Unmet Clinical Needs
Bibliographic record
Abstract
OBJECTIVE: An estimated 40-50% of patients with psoriasis (PsO) have psoriatic nail disease, which is associated with and directly contributes to a greater clinical burden and worse quality of life in these patients. In this review, we examine how recent advances in the use of new diagnostic techniques have led to improved understanding of the link between nail and musculoskeletal manifestations of psoriatic disease (PsD; e.g., enthesitis, arthritis) and we review targeted therapies for nail PsO (NP). METHODS: We performed a literature search to identify which systemic therapies approved for the treatment of PsO and/or psoriatic arthritis (PsA) have been evaluated for the treatment of NP, either as a primary or secondary outcome. A total of 1546 articles were identified on February18, 2019, and evaluated for relevance. RESULTS: We included findings from 66 articles on systemic therapies for the treatment of NP in PsD. With several scoring systems available for the evaluation of psoriatic nail disease, including varied subtypes and application of the Nail Psoriasis Area Severity Index, there was a high level of methodological heterogeneity across studies. CONCLUSION: NP is an important predictor of enthesitis, which is associated with the early stages of PsA; therefore, it is important for rheumatologists and dermatologists to accurately diagnose and treat NP to prevent nail damage and potentially delay the onset and progression of joint disease. Further research is needed to address the lack of both standardized NP scoring systems and well-defined treatment guidelines to improve management of PsD.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".