COVID-19 vaccine testing & administration guidance for allergists/immunologists from the Canadian Society of Allergy and Clinical Immunology (CSACI)
Bibliographic record
Abstract
BACKGROUND: Safe and effective vaccines provide the first hope for mitigating the devastating health and economic impacts resulting from coronavirus disease 2019 (COVID-19) and related public health orders. Recent case reports of reactions to COVID-19 vaccines have raised questions about their safety for use in individuals with allergies and those who are immunocompromised. In this document, we aim to address these concerns and provide guidance for allergists/immunologists. METHODS: Scoping review of the literature regarding COVID-19 vaccination, adverse or allergic reactions, and immunocompromise from PubMed over the term of December 2020 to present date. We filtered our search with the terms "human" and "English" and limited the search to the relevant subject age range with the term "adult." Reports resulting from these searches and relevant references cited in those reports were reviewed and cited on the basis of their relevance. RESULTS: Assessment by an allergist is warranted in any individual with a suspected allergy to a COVID-19 vaccine or any of its components. Assessment by an allergist is NOT required for individuals with a history of unrelated allergies, including to allergies to foods, drugs, insect venom or environmental allergens. COVID-19 vaccines should be offered to immunocompromised patients if the benefit is deemed to outweigh any potential risks of vaccination. INTERPRETATION: This review provides the first Canadian guidance regarding assessment of an adolescent and adult with a suspected allergy to one of the COVID-19 vaccines currently available, or any of their known allergenic components, and for patients who are immunocompromised who require vaccination for COVID-19. As information is updated this guidance will be updated accordingly.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".