Bibliographic record
Abstract
BACKGROUND: Since the advent of biologic therapies for psoriasis, reports of efficacy in nail psoriasis have appeared in the literature and at international conferences with increasing frequency. OBJECTIVE: This article aims to review the existing literature on the use of biologics in the treatment of nail psoriasis. METHODS: An extensive literature review was conducted using OVID Medline. Studies examining the efficacy of biologics in the treatment of nail psoriasis were documented. RESULTS: A literature review revealed few clinical trials specifically concentrating on nail psoriasis; however, nails have been assessed in larger clinical trials for cutaneous psoriasis. A large, multicenter, phase III, double-blind, placebo-controlled study of infliximab administered as a brief induction regimen at weeks 0, 2, and 6 followed by a single infusion every 8 weeks revealed statistically significant mean percent improvement in the Nail Psoriasis Severity Index (NAPSI) score over placebo at both week 10 (26.8% vs -7.7%, respectively; p < .001) and week 24 (57.2% vs -4.1%, respectively; p < .001). For other biologics, evidence has thus far been largely anecdotal, appearing as either case studies or extracted secondarily from open-label prospective trials in plaque psoriasis or psoriatic arthritis. CONCLUSION: Infliximab appears to be the most effective treatment for nail psoriasis to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".