A Web-Based Survey on Factors for Unvaccination and Adverse Reactions of SARS-CoV-2 Vaccines in Chinese Patients with Psoriasis
Bibliographic record
Abstract
PURPOSE: Vaccination is one of the most important strategy to prevent infections and control epidemics, but it also raises concerns about safety in patients receiving treatments. This study aimed to investigate the rate and factors for unvaccination, as well as adverse reactions and deterioration of disease after SARS-CoV-2 vaccination in psoriatic patients. METHODS: A web-based questionnaire survey on SARS-CoV-2 vaccination, adverse reactions, and self-reported change of disease condition after vaccination in patients with psoriasis was conducted. Demographic, clinical, and psychological data were collected. Multivariable logistic regression was used in the estimation of associations. RESULTS: A total of 788 psoriatic patients were investigated, and 68.9% reported SARS-CoV-2 vaccination. Younger age, use of interleukin-17 inhibitors, and symptoms of anxiety were associated with unvaccination. The incidence of overall adverse reactions after vaccination was 30.8%, and no severe adverse reaction was reported. The most common local and systemic adverse reactions were pain at the injection site and fatigue, respectively. Most patients reported no change in psoriasis after vaccination, while 16.6% and 4.4% reported slight and significant deteriorations of the disease, respectively. Nonadherence to treatment, symptoms of anxiety and depression, and perceived stress were associated with self-reported deterioration of psoriasis after vaccination. CONCLUSION: While a favorable safety profile of SARS-CoV-2 vaccines is observed, receiving biologic treatment is factor for unvaccination in patients with psoriasis. Deterioration of psoriasis reported by a small proportion of patients is partially attributable to mental and behavioral factors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".