Immunizing Patients with Adverse Events Following Immunization in the Canadian Special Immunization Clinic Network (2015–2017)
Bibliographic record
Abstract
The experience of an adverse event following immunization (AEFI) can increase vaccine hesitancy among patients and health professionals who may be concerned about the risk of a recurrent or more severe event following revaccination. Infectious disease physicians and allergists in the Canadian Special Immunization Clinic (SIC) Network developed standard protocols for evaluation and revaccination of patients with prior AEFIs. We analyzed the outcomes of patients evaluated for AEFIs from 2015 to 2017. Patients are referred to one of 11 SICs in Canada by a physician or Public Health. Inclusion criteria are: patients of any age with injection-site reaction (ISR) ≥10 cm, allergic-like events (ALE) <24h post-immunization, neurological symptoms, and other AEFIs of concern. SIC physicians evaluate eligible patients and make immunization recommendations according to standard protocols. Patients are followed up after revaccination to capture AEFI recurrence. Recommendations and outcomes of revaccination(s) are transmitted to referring providers and Public Health. Following individual consent, data are transferred to a central database for analysis. For patients with more than one AEFI, the most severe event was included in the analysis. From June 2015 to May 2017, 230 patients were referred to the network for prior AEFI and 124 patients were enrolled. Most participants (86%) were <18 years of age and 49% were female. The most common types of AEFI were ALEs (37%; 46/124), followed by ISRs (23%; 29/124), neurologic events (15%; 19/124), and other systemic events (e.g., high fever) (24%; 30/124). Revaccination was recommended for 89 (72%) patients. AEFI recurrences occurred in 9/63 (14%) patients who were revaccinated and followed up: 2/22 (9%) ALEs (both less severe than first AEFI) and 7/14 (50%) ISRs (5 less severe, 2 equally severe as first AEFI). Patients with AEFIs benefit from clinical assessment by physicians with expertise in vaccines. The results suggest that most AEFIs do not contraindicate future immunizations. The risk of AEFI recurrence is low, except for ISRs, which are generally less severe that the initial AEFI. Specialized immunization services can support health professionals in managing patients with prior AEFIs. K. A. Top, Pfizer: Investigator, Research support; GSK: Investigator, Research grant; M. Sadarangani, Pfizer: Grant Investigator, Research grant; S. A. McNeil, GSK/Merck: Grant Investigator, Investigator and Scientific Advisor, Consulting fee, Grant recipient, Research grant, Research support and Speaker honorarium; G. De Serres, GlaxoSmithKline: Investigator and Scientific Advisor, Grant recipient and travel reimbursement; Ontario Nurses Association: Consultant, compensation for expert testimony
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".