Adverse event following immunisation of adsorbed-inactivated Coronavac (Sinovac) and ChAdOx1 nCOV-19 (Astra Zeneca) of COVID-19 vaccines.
Bibliographic record
Abstract
INTRODUCTION: Countries around the world organised mass vaccinations using various types of vaccines against COVID-19, like inactivated viruses and mRNA. The study aimed to look at adverse events following immunisation (AEFI) of Coronavac® (SIN) and ChAdOx1 nCOV-19 ® (AZ) COVID-19 vaccines in Indonesia. MATERIALS AND METHODS: Subjects who received SIN or AZ vaccines were sent questionnaires twice: after they received the first and the second doses of vaccine, respectively. AEFI data on the first- and second-day post-vaccination were collected and analyzed descriptively. RESULTS: A total of 1547 people vaccinated with SIN vaccine, 529 (33.3%) responded to the first-dose and 239 (47%) to the second-dose questionnaires, whereas 936 people vaccinated with AZ vaccine, 483 (51.6%) answered the firstdose and 123 (25%) to the second-dose questionnaires. Some important AEFIs on the first- and second-day post receiving SIN vs. AZ vaccination were as follows: fever 4% vs 59%; pain at the injection site 27% vs 87%; redness and swelling at the injection site 4% vs 18%; nausea 5% vs 30%; diarrhea 1.8% vs 5.7%, respectively. CONCLUSION: SIN seemed to have fewer AEFIs than AZ. Apart from different vaccine materials and excipients, the gap in AEFIs between SIN and AZ could be caused by the distinct population where AZ recipients were more exposed to COVID-19.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".