Fear, facts, and the future: An update on coronavirus disease 2019 vaccine–induced anaphylaxis and vaccine hesitancy among those living with allergy
Bibliographic record
Abstract
Background: Despite the low incidence of coronavirus disease 2019 (COVID-19) vaccine-induced allergic reactions reported to date, concerns of such reactions have been reported in the literature among individuals with and without a history of allergic disease. Objectives: Herein, we provide an update to a previous scoping review published by our group, focusing on COVID-19 vaccine hesitancy in relation to allergy and the incidence of anaphylactic reactions to COVID-19 vaccines. Methods: protocol drafted in accordance with Arksey and O'Malley's framework for methodological reviews. A comprehensive search was conducted on 4 scientific databases (CINAHL, PsycINFO, MEDLINE, Embase) to identify eligible studies published since our initial review. Eligible articles included those published between February 12, 2022, and November 10, 2023, and were retrieved using an established search process developed by content and methodological experts. Among the 2,099 unique citations, 45 articles (2.1%) were included. Results: Consistent with previously reviewed literature, COVID-19 vaccine-induced anaphylaxis remains rare among both those with allergies and the general population. Despite the rarity of anaphylaxis, hesitancy persists among individuals with and without allergies. Conclusions: To prepare for future pandemics, it is evident that more efforts are needed to address concerns regarding the potential for allergic reactions following vaccination. As part of this process, it is important to ensure medical professionals are updated as new information becomes available and that evidence-based risk communication is accurate, accessible, and culturally appropriate.
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 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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".