The Blind Spot in COVID-19 Vaccination Policies: Under-Reported Adverse Events
Bibliographic record
Abstract
Case reports involving two academic researchers suggest that adverse events (AEs) to COVID-19 messenger RNA (mRNA) vaccination are largely underreported due to numerous clinical, systemic, political and media factors. The lack of proper analysis and consideration of the reported AEs also suggests that these injections are not as safe as widely purported. The resulting biased risk-benefit assessment may only produce misinformed public health recommendations and misguided political decisions, thereby exposing the population to an underestimated risk, in possible violation of the precautionary principle and of the right to a free and informed consent. The possible mechanisms underlying AEs to COVID-19 vaccination raise serious concerns regarding the new vaccine application of the mRNA technology that need to be addressed before expanding it to other infectious diseases. The legal considerations of AE underreporting are also discussed, and recommendations are formulated. AEs to mRNA injections are a reality and need to be better assessed than heretofore, diagnosed and reported to public health authorities for follow-up investigation in order to inform policy decisions and updates to physician guidelines in an objective, scientifically based, independent, and transparent manner.
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 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 | 0.073 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".