Immunizing Patients With Adverse Events After Immunization and Potential Contraindications to Immunization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it