2022 American College of Rheumatology Guideline for Vaccinations in Patients With Rheumatic and Musculoskeletal Diseases
Bibliographic record
Abstract
OBJECTIVE: To provide evidence-based recommendations on the use of vaccinations in children and adults with rheumatic and musculoskeletal diseases (RMDs). METHODS: This guideline follows American College of Rheumatology (ACR) policy guiding management of conflicts of interest and disclosures and the ACR guideline development process, which includes the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. It also adheres to the Appraisal of Guidelines for Research and Evaluation (AGREE) criteria. A core leadership team consisting of adult and pediatric rheumatologists and a guideline methodologist drafted clinical population, intervention, comparator, outcomes (PICO) questions. A review team performed a systematic literature review for the PICO questions, graded the quality of evidence, and produced an evidence report. An expert Voting Panel reviewed the evidence and formulated recommendations. The panel included adult and pediatric rheumatology providers, infectious diseases specialists, and patient representatives. Consensus required ≥70% agreement on both the direction and strength of each recommendation. RESULTS: This guideline includes expanded indications for some vaccines in patients with RMDs, as well as guidance on whether to hold immunosuppressive medications or delay vaccination to maximize vaccine immunogenicity and efficacy. Safe approaches to the use of live attenuated vaccines in patients taking immunosuppressive medications are also addressed. Most recommendations are conditional and had low quality of supporting evidence. CONCLUSION: Application of these recommendations should consider patients' individual risk for vaccine-preventable illness and for disease flares, particularly if immunosuppressive medications are held for vaccination. Shared decision-making with patients is encouraged in clinical settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".