Canadian Rheumatology Association Recommendation for the Use of COVID-19 Vaccination for Patients With Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To develop guidance on the use of coronavirus disease 2019 (COVID-19) vaccines in patients with autoimmune rheumatic diseases (ARD). METHODS: The Canadian Rheumatology Association (CRA) formed a multidisciplinary panel including rheumatologists, researchers, methodologists, vaccine experts, and patients. The panel used the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Outcomes were prioritized according to their importance for patients and clinicians. Evidence from the COVID-19 clinical trials was summarized. Indirect evidence for non-COVID-19 vaccines in ARD was also considered. The GRADE evidence-to-decision (EtD) framework was used to develop a recommendation for the use of the 4 COVID-19 vaccines approved in Canada as of March 25, 2021 (BNT162b2, mRNA-1273, ChAdOx1, and Ad26.COV2.S), over 4 virtual panel meetings. RESULTS: The CRA guideline panel suggests using COVID-19 vaccination in persons with ARD. The panel unanimously agreed that for the majority of patients, the potential health benefits of vaccination outweigh the potential harms in people with ARDs. The recommendation was graded as conditional because of low or very low certainty of the evidence on the effects in the population of interest, primarily due to indirectness and imprecise effect estimates. The panel felt strongly that persons with autoimmune rheumatic diseases who meet local eligibility should not be required to take additional steps compared to people without ARDs to obtain their vaccination. Guidance on medications, implementation, monitoring of vaccine uptake, and research priorities are also provided. CONCLUSION: This recommendation will be updated over time as new evidence emerges, with the latest recommendation, evidence summaries, and EtD available on the CRA website.
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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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".