Advancing the Science of Vaccine Safety During the Coronavirus Disease 2019 (COVID-19) Pandemic and Beyond: Launching an International Network of Special Immunization Services
Bibliographic record
Abstract
Within 2 years after the start of the coronavirus disease 2019 (COVID-19) pandemic, novel severe acute respiratory syndrome coronavirus 2 vaccines were developed, rigorously evaluated in large phase 3 trials, and administered to more than 5 billion individuals globally. However, adverse events of special interest (AESIs) have been described post-implementation, including myocarditis after receipt of messenger RNA (mRNA) vaccines and thrombosis with thrombocytopenia syndrome after receipt of adenoviral vector vaccines. AESIs are rare (<1 to 10/100 000 vaccinees) and less frequent than COVID-19 complications, though they have associated morbidity and mortality. The diversity of COVID-19 vaccine platforms (eg, mRNA, viral vector, protein) and rates of AESIs both between and within platforms (eg, higher rate of myocarditis after mRNA-1273 vs BNT162b2 vaccines) present an important opportunity to advance vaccine safety science. The International Network of Special Immunization Services has been formed with experts in vaccine safety, systems biology, and other relevant disciplines to study cases of AESIs and matched controls to uncover the pathogenesis of rare AESIs and inform vaccine development.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".