A Psoriatic Patient-Based Survey on the Understanding of the Use of Vaccines While on Biologics During the COVID-19 Pandemic
Bibliographic record
Abstract
BACKGROUND: Psoriatic patients who are actively receiving biologic treatment have protocols in place to achieve optimal immunity. Inactivated vaccines are safe to use during biological treatment, without interruption. Conversely, live vaccines are used with caution and likely interruption of treatment. Given the novel coronavirus (SARS-CoV-2), nation-wide administration of vaccinations is underway. OBJECTIVE: This survey gathered information on the level of education psoriatic patients have concerning vaccinations. METHODS: An electronic survey was sent to 661 patients suffering from psoriasis. Patients originated from a single solo-practitioner community-based dermatology practice. RESULTS: The average percentage of patients who understand the difference between live and inactivated vaccines between the control and study group was 36.6%. The average response to not knowing the difference between the vaccines was 36.6% and 26.6% were "unsure." When asked if it was possible to receive inactivated vaccines while on a biologic, the mass response amidst the control and study group was "unsure" (66.9%). CONCLUSION: This questionnaire demonstrates that there is a need for supplementary education about vaccines for psoriatic patients on a biologic. Physicians will need to counsel their patients on the use of potential vaccines for SARS-Cov2 while on biologics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".