Physician Perspectives on Vaccination in Patients With Autoimmune Inflammatory Rheumatic Diseases: An International Survey
Bibliographic record
Abstract
OBJECTIVE: To evaluate the perspective of physicians who care for patients with autoimmune inflammatory rheumatic disease (AIIRD) toward vaccination. METHODS: Physicians who care for patients with AIIRD were invited to participate in an online survey regarding their vaccination perspectives in adult patients with AIIRD. RESULTS: Survey responses of 370 physicians from Asia (41.1%), North America (41.6%), Europe (13.8%), and other countries (3.5%) were analyzed. Participants stated that rheumatologists (58.2%) should be primarily responsible for vaccination coverage, followed by general internists (19.3%) and family medicine practitioners (12.8%). Additionally, 96.7% of participants considered vaccination very important (≥ 4/5 rating) for patients with AIIRD. Despite these sentiments, only one-third (37%) reported vaccinating the majority (≥ 60%) of their patients. CONCLUSION: Physicians who care for patients with AIIRD agree that vaccines are effective and safe in patients with AIIRD. Unfortunately, they often do not ensure that their patients are adequately vaccinated. Further studies are needed to investigate how to improve vaccination coverage for this high-risk patient population.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".