The role of co-morbidities in the development of an AEFI after COVID-19 vaccination in a large prospective cohort with patient-reported outcomes in the Netherlands
Bibliographic record
Abstract
BACKGROUND: The effect of a preexisting comorbidity on the occurrence of adverse events after immunization (AEFIs) has been studied poorly. In this longitudinal cohort study, we assess the association between co-morbidities and the occurrence of AEFIs after COVID-19 vaccination. Also, we described the occurrence of flare-ups and their manifestation after COVID-19 vaccination in people with rheumatic diseases. RESEARCH DESIGN AND METHODS: We performed multivariable logistic regression to investigate the association between the occurrence of AEFIs and 10 common comorbidities using patient-reported data from people vaccinated with the AstraZeneca, Johnson&Johnson, Moderna, or Pfizer vaccine. RESULTS: Occurrence of any AEFI, injection site reactions, headache, fatigue, and/or malaise was significantly associated with presence of comorbidities, including psychological disorders, musculoskeletal disorders, and endocrine disorders after the first and second doses (OR ranges 1.23-1.77). One participant with rheumatoid arthritis experienced a flare-up after receiving the first dose of the AstraZeneca vaccine. DISCUSSION/CONCLUSION: The results showed that the odds of reporting an AEFI after COVID-19 vaccination is significantly higher in the presence of some comorbidities whilst flare-ups are uncommon after receiving COVID-19 vaccination in people with rheumatic disease. In-depth research is needed to validate our results and unravel the observed associations from a mechanistic perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".