Immunizing Patients With Adverse Events After Immunization and Potential Contraindications to Immunization
Bibliographic record
Abstract
BACKGROUND: For patients who have experienced adverse events following immunization (AEFI) or who have specific medical conditions, there is limited evidence regarding the best approach to immunization. The Special Immunization Clinics (SICs) Network was established to standardize patient management and assess outcomes after reimmunization. The study objective was to describe the first 2 years of the network's implementation. METHODS: Twelve SICs were established across Canada by infectious diseases specialists and allergists. Inclusion criteria were as follows: local reaction ≥ 10 cm, allergic symptoms < 24 hours postimmunization, neurologic symptoms and other AEFI or medical conditions of concern. Eligible patients underwent a standardized evaluation, causality assessment was performed, immunization recommendations were made by expert physicians and patients were followed up to capture AEFI. After individual consent, data were transferred to a central database for analysis. RESULTS: From June 2013 to May 2015, 151 patients were enrolled. Most were referred for prior AEFI (132/151, 87%): 42 (32%) for allergic-like reactions, 31 (23%) for injection-site reactions, 20 (15%) for neurologic symptoms and 39 (30%) for other systemic symptoms. Nineteen patients (13%) were seen for underlying conditions that complicated immunization. Reimmunization was recommended for 109 patients, 60 of whom (55%) were immunized and followed up. Eleven patients (18%) experienced recurrence of their AEFI; none were serious (eg, resulting in hospitalization, permanent disability or death). CONCLUSIONS: The most frequent reasons for referral to a SIC were allergic-like events and injection site reactions. Reimmunization was safe in most patients. Larger studies are needed to determine outcomes for specific types of AEFI.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".