ArticleVaccines2022
Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach.
Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Circadian Variation in the Response to Vaccination: A Systematic Review and Evidence Appraisal.Journal of biological rhythms · 2024Pooled it
- A Review of Data Engineering in United States Healthcare Infrastructure.Healthcare (Basel, Switzerland) · 2026Review
- Article
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Utilizing group-based models to identify adverse event patterns after an intervention.Frontiers in medicine · 2025Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Daily variation in blood glucose levels during continuous enteral nutrition in patients on the intensive care unit: a retrospective observational study.EBioMedicine · 2024Observational
- Exploring post-COVID-19 health effects and features with advanced machine learning techniques.Scientific reports · 2024Article
- Article
- The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.Frontiers in public health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Side effects of COVID-19 or other vaccinations may affect an individual's safety, ability to work or care for self or others, and/or willingness to be vaccinated. Identifying modifiable factors that influence these side effects may increase the number of people vaccinated. In this observational study, data were from individuals who received an mRNA COVID-19 vaccine between December 2020 and April 2021 and responded to at least one post-vaccination symptoms survey that was sent daily for three days after each vaccination. We excluded those with a COVID-19 diagnosis or positive SARS-CoV2 test within one week after their vaccination because of the overlap of symptoms. We used machine learning techniques to analyze the data after the first vaccination. Data from 50,484 individuals (73% female, 18 to 95 years old) were included in the primary analysis. Demographics, history of an epinephrine autoinjector prescription, allergy history category (e.g., food, vaccine, medication, insect sting, seasonal), prior COVID-19 diagnosis or positive test, and vaccine manufacturer were identified as factors associated with allergic and non-allergic side effects; vaccination time 6:00-10:59 was associated with more non-allergic side effects. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of vaccination.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.